College of Graduate Studies Courses

Biomedical Data Science & Informatics

This course is delivered through Clemson University as (CPSC 6030 Data Visualization) as part of a joint Biomedical Data Science and Informatics program. Analyzing and understanding data is a crucial task of many applications including science, engineering, and education. When questions about data are well-defined, answers can be found by using computational and statistical techniques. However, many problems are ill-specified and require the human's perceptual capabilities to be solved. Hence the need for visualizing, transforming and representing abstract data in a form that facilitates human interactions and understanding. The aim of this course is that of introducing the theory and practice of data visualization. Students will learn to design and evaluate effective visualizations by learning techniques and visualization idioms. Moreover, students will acquire hands-on experience using visualization frameworks and dedicated programming libraries. This class provides an introduction to data visualization by focusing on design principles for creating effective visualizations of abstract data. This class targets students at the senior or master level. The set of skills learned in this class are fundamental for any student interested in a career in data science, data analytics, business, and many others.

3 credits

This course is delivered through Clemson University as CPSC 6140 Human and Computer Interaction as part of a joint Biomedical Data Science and Informatics program. This 3-credit hour course is an introduction to human-computer interaction (HCI) and focuses primarily on user-centered design techniques. Students will work on a semester-long team-based project identifying a problem in a novel domain, interviewing, understanding the stakeholders, and subsequently develop and test a prototype. Students are expected to be proficient in programming in Java, C, and C++ since the pre-requisites for this class are CPSC 2120 and CPSC 2150.

3 credits

This course is delivered through Clemson University as CPSC 6300 Applied Data Science as part of a joint Biomedical Data Science and Informatics program.Welcome to CPSC6300-002 Applied Data Science. This is a 3-credit graduate level course whose focus is on the analysis of messy, real life data using statistical and machine learning methods. This course aims to help student to grasp the fundamental concepts and applied methods in Data Science and develop skills needed in the five key facets of a data science project: data collection, data management, exploratory data analysis, statistical and machine learning, and communication.

3 credits

This course is delivered through Clemson University as BIOE 6310 Medical Imaging as part of a joint Biomedical Data Science and Informatics program." Introduction to the history, physics, and instrumentation of various medical imaging modalities, including X-ray, Computed Tomography, Magnetic Resonance Imaging, and Ultrasound. Students will learn the principle of medical imaging from an engineering and clinical perspective. Students will have the opportunity to evaluate medical images acquired from clinics and to explain the clinical applicability among different modalities.

3 credits

This course is delivered through Clemson University as GEN 6400 Bioinformatics as part of a joint Biomedical Data Science and Informatics program." Bioinformatics is an interdisciplinary field that develops and applies computational and statistical methods to solve biological problems. A major theme in bioinformatics is to integrate and mine the "big data" generated by genome sequencing projects and other high throughput studies. Bioinformatic methods and software tools are developed to unravel fundamental mechanisms underlying the structure and function of macromolecules, biochemical pathways, diseases, and genome evolution. Many bioinformatic applications require computation and programming. Although basic programming concepts will be introduced in this course, students are not required to write their own software programs to solve biological problems. However, it is expected that all students learn how to use the available software tools to solve biological problems. The aim is to emphasize critical thinking: understanding how these tools work in principle and developing a computational mind for solving biological problems. Topics to be discussed in this course include: genome annotation, biological databases and information retrieval, sequence analysis algorithms, protein structural bioinformatics, gene expression and network analysis, metabolic pathway analysis, and systems biology. A tentative schedule of lectures is shown at the end of the syllabus.

3 credits

This course is delivered through Clemson University as (MATH 6410) as part of a joint Biomedical Data Science and Informatics program. This course will cover ideas from Simulation, Discrete-time Markov chains, Poisson processes, and Continuous-time Markov chains. Concepts from probability theory will be reviewed as needed, but you should be comfortable with the majority of the material covered in MATH 4000 before taking this course. This course will have a strong modeling and computational focus, however, you will also be expected to learn how to derive simple facts on your own.

3 credits

This course is delivered through Clemson University as (CPSC 6420 Artificial Intelligence) as part of a joint Biomedical Data Science and Informatics program. This course presents fundamental concepts in Artificial Intelligence. Specific topics include uninformed and informed search techniques, game playing, Markov decision processes, reinforcement learning, uncertain knowledge and probabilistic reasoning, constraint satisfaction problems, and supervised learning. Students must be familiar with principles of probability and statistics and must have programming experience when enrolling in this course.

3 credits

This course is delivered through Clemson University as CPSC 6430 Machine Learning as part of a joint Biomedical Data Science and Informatics program." This course provides a broad introduction to machine learning. We will especially be interested in using mathematical models to explain why and when certain methods/models work, solving real-world problems by the models.

3 credits

This course is delivered through Clemson University as CPSC 6620 Database Management Systems as part of a joint Biomedical Data Science and Informatics program." The problem-based instruction approach, TEXNH, will be used in this course. Students are expected to learn database concepts through a semester-long multimedia database project. Specifically, students will be required to implement an online multimedia database system, MeTube, which is essentially a modified version of the popular YouTube system (http://www.youtube.com), while learning database theories and technologies. Web technologies necessary for implementing the project will also been studied.

3 credits

This course is delivered through Clemson University as GEN 6700 Human Genetics as part of a joint Biomedical Data Science and Informatics program." Basic principles of inheritance; population, molecular and biochemical genetics; cytogenetics; immunogenetics; complex traits; cancer genetics; treatment of genetic disorders; genetic screening and counseling; and the Human Genome Project. Prerequisites: Permission of the instructor

3 credits

This course is mandatory for students in Clemson-MUSC Biomedical Data Science and Informatics students. The overall goal of the course is to expose students to a broad range of concepts, theories, methods, and practices in biomedical data science and informatics, and the specific research topics pursued by the faculty in the program. The students will learn to comprehend and present scientific literature in this field.

1 credit

This is an introductory course to provide students an overview of the biomedical informatics filed. Students will learn fundamental theories and concepts of bioinformatics, clinical research informatics, health informatics, consumer health informatics, and public health informatics. Students will learn informatics tools, techniques, and approaches for research and health care. The course is taught by a variety of informatics experts. The course is required for BDSI Ph.D. students and is open to other students interested in understanding of biomedical informatics. No previous informatics or computer science experience is required.

3 credits

This course is mandatory for students enrolled in the Clemson-MUSC Biomedical Data Science and Informatics (BDSI) program. It introduces data science and informatics students, clinicians, and public health practitioners to fundamental principles of data standards and terminologies and their application for exchange and meaningful use of health data and information using standard API technology. Use of standards and terminologies is critical for interoperability and is required for meaningful use of data, both for primary use (i.e., patient care) as well as secondary use for quality monitoring, public health reporting, decision support, research and analysis. A good understanding of the role, use, and benefits of data standards and terminologies is crucial for all using healthcare data and information for research and other purposes, and for all involved in the development of applications generating or consuming such data and information. This course provides foundational knowledge and introduces students to the skills, resources, and concepts allowing them to be a life-long learner and work in this evolving field.

3 credits

This course will provide an overview of precision medicine informatics with a focus on cancer. We will cover current initiatives and efforts to use health informatics to individualize care. The integration of heterogeneous data sets from different measurements such as the exposure, metabolome, genome, proteome, and other laboratory measurements is central to the goal of treating each patient as an individual in regard to precision treatment. The use of next generation sequencing, transcriptomic and other detailed data sets will move us to more precise characterizations of patients and ultimately more precise treatments. To get there we will need to understand the informatics of big data and learning from high dimensional data sets. As a use case, we will do a detailed examination of precision medicine clinical trials in cancer. We will also examine publicly available data to understand how high throughput measurement techniques are used and the methods that are applied to them to more precisely characterize cohorts of patients. Lastly, we will examine the challenges of precision medicine to explore ways to integrate the approaches into clinical healthcare systems.

3 credits

This course will provide an over view of clinical and translational research informatics. Students taking this course will learn about research data management, relational database design, modern research data capture tools, best practices, clinical data warehousing, security risks and mitigations, privacy issues in electronic data, data standards, data mining and other related topics. Students will get hands-on experience with using modern translational research informatics tools such as REDCap, i2b2 and others

3 credits

Laboratory rotations are required for all BDSI Ph.D. students. A minimum of 2 rotations with two different laboratories are required, typically during the fall, spring, or subsequent summer of the first year. Three rotations with three different labs are encouraged to enrich students’ research experience and exposure to maximal faculty members’ research.

2 credits

This course will provide an introduction to methods in statistical learning that are commonly used to extract important patterns and information from biomedical data. Topics include, linear methods for regression and classification, regularization, kernel smoothing methods, statistical model assessment and selection, and support vector machines. Unsupervised learning techniques such as principal component analysis and generalized principal component analysis will also be discussed. The applications will be illustrated using the statistical programing language.

3 credits

This course provides an introduction to Natural Language Processing (NLP), as applied to clinical text (i.e., narrative free text found in Electronic Health Record systems). It includes an overview of the specificities of clinical text (i.e., narrative text documents in the electronic health record), and focuses on information extraction (IE) methods (e.g., pattern-matching methods, machine learning methods), existing applications, and resources for information extraction. The actual development, training, and evaluation of a simple information extraction application is an optional practicum concluding this course. This practicum is recommended and is required for the 3 credits version of the course.

2 credits

This course is concerned with analysis of microbiome data enabled by high-throughput sequencing technologies. We will briefly cover foundational concepts in microbial ecology, molecular biology, bioinformatics, and DNA sequencing. The main focus of the course will be on developing an understanding of multivariate analysis of microbiome data. Practical skills to be developed in this course include managing high-dimensional and structured data in metagenomics, visualization and representation of high-dimensional data, normalization, filtering, and mixture-model noise modeling of count data, as well as clustering and predictive model building. The topics in this course are developed only as far as to enable the users to understand the merits of these analyses. The main goal is to give the students an intuition about when certain analyses are applicable and practical ways to implement these analyses. A deeper understanding of these methods can be achieved by taking additional classes in statistics such as ‘Statistical Methods for Bioinformatics’ and ‘Multivariate Analysis’, which cover a much broader range of topics in more rigorous detail. Familiarity with R will be readily acquired during the course.

2 credits

This course is mandatory for SC BIDS4Health Clemson-MUSC training program trainees. The overall goal of the course is to expose students to a broad range of concepts, theories, methods, and practices concerning rural health, under-served populations, and health inequities. Developing understanding of possible solutions for inequities and other issues through biomedical data science and informatics is another goal. The students will learn to comprehend and present scientific literature in this field.

1 credit

This course is mandatory for SC BIDS4Health Clemson-MUSC training program trainees and open to other qualified and interested students. In this mentored practicum experience, trainees will identify a specific problem that plagues a rural or medically underserved community or health organization of their choice and implement a informatics and/or data science solution to evaluate, alleviate, or resolve some aspect of that problem.

3 credits

This course is mandatory for SC BIDS4HEALTH Clemson-MUSC training program trainees and open to BDSI and other qualified and interested students. In this preceptorship experience, the trainees with practice teaching topics in biomedical data science and informatics to students at partner undergraduate institutions.

1 credit

This course is mandatory for SC BIDS4Health Clemson-MUSC training program trainees and open to BDSI and other qualified and interested students. The trainees will get involved in direct, guided mentorship experiences that leverage the relationship between the program and partnering undergraduate research experience programs( currently at MUSC, SCSU, Clemson, University at Buffalo). Through structured weekly assignments, experiences, and presentations the trainees will start developing strategies to help others grown in their scientific careers.

1 credit

This practical course will introduce students to the process of writing a review article, which is a fundamental part of synthesizing research findings. Students are expected to work in groups in creation of publishable review, which will strengthen their publication portfolio. The course will include step-by-step information on how to perform screening and extraction, how to use library search engines, how to use citation managers, and other relevant information.

3 credits

This course is optional for students enrolled in the Clemson-MUSC Biomedical Data Science and Informatics (BDSI) program. It provides an opportunity for studying unique topics involved in the learning and research activities of doctoral students. Under the supervision of a faculty member in the BDSI program, the student would propose a topic with a description of the learning process and evaluation. Effort between 1.0 and 3.0 semester credits are possible. If approved, the student would proceed with the proposed course with supervision from the faculty member.

3 credits

This course is delivered through Clemson University as IE 8000 Human Factors Engineering as part of a joint Biomedical Data Science and Informatics program. This course presents a graduate level introduction to Human Factors and Ergonomics and introduces designing for human use, taking into consideration both the human and engineering capabilities and limitations. Ergonomics and human characteristics such as body size, sensory abilities, memory, etc. must be considered when designing human-machine systems.

3 credits

This course is delivered through Clemson University as (STAT 8010) as part of a joint Biomedical Data Science and Informatics program. Course Objectives: At the end of this course, the student will be able to:1. Summarize and interpret research data.2. Apply statistical techniques and knowledge appropriately.3. Select and implement several basic experimental designs.4. Draw appropriate conclusions and inferences from data.

3 credits

This course is delivered through Clemson University as CPSC 8030 Scientific visualization as part of a joint Biomedical Data Science and Informatics program."

3 credits

This course is delivered through Clemson University as MATH 8050 Data Analysis as part of a joint Biomedical Data Science and Informatics program.Upon successful completion of the course, a student should be able to: Use and understand basic statistical methods in reports and experimentation; Fit simple and multiple linear regression models; Perform model diagnostics and inference; Perform statistical calculations using the statistical software package R.

3 credits

This course is delivered through Clemson University as HLTH 8090 Epidemiological Research as part of a joint Biomedical Data Science and Informatics program." Basic concepts of epidemiology with emphasis on applied aspects rather than theoretical. Examples are drawn from clinical practice. Use of relevant PC-based computer packages is required. May also be offered as MICR 8090.

3 credits

This course is delivered through Clemson University as HLTH 8100 Medical Imaging as part of a joint Biomedical Data Science and Informatics program." Catalog description: Provides experience in analysis of decisions in health-care management policy, problems, resources and alternative courses of action for health service organizations. Students participate in analysis of organization objectives and means for achieving health service goals. The course introduces various conceptual and analytical frameworks for understanding, developing and evaluating a range of health policy areas. The course begins by building a foundation to understand policy from theories to application. Students will then choose their own policy area to work on. They will lead class discussion to rationally analyze health policy and ultimately propose modification or changes to policy. Policy is multi-dimensional and can be viewed from various lenses, varying from historical to political, from social to legal perspective. This course will focus on the economic and ethical ramifications of health policy. As the role of evidence has become increasingly important in healthcare planning and services, priority is given to building knowledge and skill to support policy making based on empirical findings.

3 credits

This course is delivered through Clemson University as HLTH 8110 Health Care Delivery Systems as part of a joint Biomedical Data Science and Informatics program. This course focuses on healthcare delivery systems in the United States. The course content will be both descriptive and analytical. The descriptive part will cover a wide range of topics such as the evolution and distinctive features of healthcare services in the U.S., health services professionals, and outpatient and primary care. The analytical part will look into a number of critical issues including the cost & benefit of medical technology, health insurance, reimbursement mechanisms, and integration of delivery networks. Some industrialized countries' healthcare systems will also be examined to contrast with the U.S. systems.

3 credits

This course is delivered through Clemson University as (HLTH 8130) as part of a joint Biomedical Data Science and Informatics program. Introduction to the multiple determinants of health, including medical care, socioeconomic status, the physical environment, and patterns of individual and collective behavior. Review of the definition of population health and ethical, legal, social and managerial issues affecting population health improvement. Development of interventions to improve population health.

3 credits

This course is delivered through Clemson University as CPSC 8200 as part of a joint Biomedical Data Science and Informatics program. Parallel computer architectures are ubiquitous today, adopted by all computer systems ranging from mobile devices like cellphones, laptops to data centers. In this course, we examine various parallel architectures, networking, and the programming model that they support. The topics covered in this course include, but are not limited to: Advanced computer architecture: multicore, manycore, heterogeneous architectures, shared memory architectures, distributed memory architectures, computer clusters, and data centers Networking: line, ring, grid, torus, etc. System software: resource management, workload scheduling, data storage, and file systems Programming models: multithreading, message passing, PGAS, MapReduce, CUDA Performance measurement, benchmarking, and analysis.

3 credits

This course is delivered through Clemson University as HLTH 8210 Health Research I: Design & Measurement as part of a joint Biomedical Data Science and Informatics program. Addresses issues in research design, measurement, project planning, data collection and data management for health research. Topics include experimental and quasi-experimental design, measurement theory applied to key health concepts, survey methodology, observational research and research ethics.

3 credits

This course is delivered through Clemson University as (HCC 8310) as part of a joint Biomedical Data Science and Informatics program. This course is an introduction to Human-Centered Computing. It is intended for students pursuing a PhD in HCC. The primary objective of this course is to facilitate the acquisition of essential skills for studying and conducting research in Human-Centered Computing. Specifically, the goal of the course is to introduce you to theoretical perspectives in HCC. The goals of the course will be accomplished through the combination of readings, discussions, lectures, projects, assignments, and exercises. Students are encouraged to pursue and discuss their own research interests as part of the course. Because collaboration is critical to successful HCC projects, group work is emphasized. Upon completion of this class you should have the knowledge and skills to enable you to determine an appropriate theoretical frame for your research, identify user needs, designing based on identified human-centered needs, iteratively improve upon your design, build out your design and evaluate your design.

3 credits

This course is delivered through Clemson University as HLTH 8310 Quantitative Analysis in Health Research I as part of a joint Biomedical Data Science and Informatics program. Course focuses on the application of statistical methods to health research questions using health survey data. Course material is built on knowledge gained from previous courses in research design methods and statistics. Students gain hands-on experience in investigating health research questions and analyzing complex health data sets.

3 credits

This course is delivered through Clemson University as (CPSC 8380 Advanced Data Structures) as part of a joint Biomedical Data Science and Informatics program. This is a course on advanced Data structures and their applications in executing algorithms for varied applications. It is suitable for beginning graduate students and/or serious seniors. The objective is to familiarize the audience with the fundamental concepts, techniques and tools of advanced data structures and their use in algorithms. Participation in this course will enable you to harness the power of advanced concepts of data structures & algorithms in your own areas of application as well as will prepare you to take advanced courses and/or do research work in any specific area of specific applications.

3 credits

This course is delivered through Clemson University as CPSC 8430 Deep Learning as part of a joint Biomedical Data Science and Informatics program. This is a special topics course in deep learning architectures. Deep learning is either a pattern classification or feature representation learning technique that has multiple levels of non-linear operations. This course will cover algorithms in Deep Learning, such as convolutional neural networks, recursive neural networks, generative adversarial nets, and deep reinforcement learning., as well as application areas, such as image and NLP.

3 credits

This course is delivered through Clemson University as (GEN 8500) as part of a joint Biomedical Data Science and Informatics program. ” This is an introductory course that covers theory and analysis of complex traits from the genetic standpoint and does not assume any prior knowledge of the subject. The topics treated will tentatively be the following: Phenotypic, genetic, and environmental variation; Phenotypic model; additive, dominance, epistatic effects; population mean; Average and substitution effects; breeding value and dominance deviation; complications from epistasis; Variance and its components: phenotypic, genetic, and environmental; Inbreeding and its effect on means and variances; heterosis; Concepts and properties of heritability; resemblance between relatives and heritability estimation for quantitative and binary traits; Correlations among traits: phenotypic, genetic, and environmental; Quantitative Trait Locus (QTL) mapping by linkage and association; Artificial selection and its response. Relevant articles from the literature may also be used as a teaching tool.

3 credits

This course is delivered through Clemson University as (HLTH 8600) as part of a joint Biomedical Data Science and Informatics program. This course is designed as a 3-credit-hour course that students can take as a traditional instructor-led learning experience. The course focused on the clinical decision support system (CDSS), its definitions, types, front-end use, backend design, development, maintenance, the mechanism of CDSS, the evaluation of CDSS itself, and evaluation of care quality and clinical outcomes associated with CDSS, the implementation and deployment of CDSS, the legal and ethical aspects of CDSS, and emerging topics that are related to CDSS. Each week, the course will include the following components: lectures (45-minute didactic lectures), quizzes (self-graded, multiple-choice questions), readings (textbook chapters and newly published papers), exercises, discussion points/topics, and projects/assignments to design, test, and write the plans. Students or professionals can also use the course to learn specific topics covered in the CDSS course. The targeted students include healthcare professionals, medical students, other students in health fields, or students with computer science or information science backgrounds.

3 credits

This course is delivered through Clemson University as CPCS 8650 as part of a joint Biomedical Data Science and Informatics program. Data mining has emerged as one of the most exciting and dynamic fields in computer science, bioinformatics, industrial engineering, etc. The driving force for data mining is the available of massive data that potentially contain valuable bits of hidden knowledge. Such data include consumer data, transaction histories, medical records, biological experiments, Web information, Network information, etc. Commercial enterprises have been quick to recognize the value of data mining; consequently, within the span of a few years, the software market for data mining has expanded to be in excess of tens of billions of dollars. This course is designed to provide graduate students with a broad knowledge in the design and use of data mining algorithms, exposure to data mining research, and hands-on practices in applying these ideas to a real-life situation.

3 credits

This course is delivered through Clemson University as (CPSC 8710 Foundations of Software Engineering) as part of a joint Biomedical Data Science and Informatics program. Techniques and issues in software design and development; tools, methodologies and environments for effective design, development and testing of software; organizing and managing the development of software projects. Preq: Enrollment in Computer Science program.

3 credits

This course is delivered through Clemson University as (HLTH 8900) as part of a joint Biomedical Data Science and Informatics program." The course provides an overview of the emerging population health informatics. Population health informatics is the applications of computer sciences and information technologies in population health to facilitate policy planning, decision making, and implementations to improve the health and wellness of different groups of the population.

3 credits

This course is delivered through Clemson University as GEN 8900 Introduction to Quantitative Genetics as part of a joint Biomedical Data Science and Informatics program." This is an introductory course that covers theory and analysis of complex traits from the genetic standpoint and does not assume any prior knowledge of the subject. The topics treated will tentatively be the following: . Phenotypic, genetic, and environmental variation. . Phenotypic model; additive, dominance, epistatic effects; population mean. . Average and substitution effects; breeding value and dominance deviation; complications from epistasis. . Variance and its components: phenotypic, genetic, and environmental. . Inbreeding and its effect on means and variances; heterosis. . Concepts and properties of heritability; resemblance between relatives and heritability estimation for quantitative and binary traits. . Correlations among traits: phenotypic, genetic, and environmental. . Quantitative Trait Locus (QTL) mapping by linkage and association. . Artificial selection and its response.

3 credits

Students conduct research under the guidance of their mentor.

1 credit

Biochemistry

Current and emerging topics in the biomedical sciences will be presented and discussed in a journal club format. One student per week will lead a group discussion of a faculty-approved article from a high impact journal. All students are expected to read the selected paper in advance of the class and to actively participate in the discussion. Students are expected to attend each class and to present at least one journal article per semester. All students are expected to actively participate in the discussion. Grading (P/NP) for presentation and participation will be based on the rubric outlined in the syllabus. Students who do not participate in the discussion will be given notice that they need to increase their interaction with the group.

1 credit

In this series, students give a seminar based on their own research to their fellow students, graduate training committee, thesis committee, faculty and post-doctoral fellows in the Department of Biochemistry. This is a great opportunity for the students to present their work in an informal setting and to receive feedback on his/her studies from a large audience with different scientific backgrounds. Students are required to give at least two seminars during their training.

1 credit

Tumor growth, invasion, and metastasis depends on the interplay between cancer, stromal, and immune cells. This course will examine the relationship between molecular and cellular components of the tumor microenvironment and extracellular matrices, and discuss how tumor growth is affected by organismal factors like hormones and immune cells. After developing a detailed and mechanistic understanding of the microenvironment in which cancer cells interact, students will learn how these interactions influence cancer therapies and immunotherapies.

1 credit

This course provides exposure to cutting edge cancer research using a combination of seminars and journal club discussions. Seminars will feature presentations on the latest discoveries by leading experts from around the country. Journal clubs will provide an in depth discussion of research articles that relate to each seminar topic or highlight important advances in cancer research.

1 credit

Tumor growth, invasion, and metastasis depends on the interplay between cancer, stromal, and immune cells. This course will examine the relationship between molecular and cellular components of the tumor microenvironment and extracellular matrices, and discuss hoe tumor growth is affected by organismal factors like hormones and immune cells. After developing a detailed and mechanistic understanding of the microenvironment in which cancer cells interact students will learn how these interactions influence cancer therapies and immunotherapies.

1 credit

Genomic instability is defined as an increased tendency for cells to acquire mutations and other genomic alterations. Most cancers are characterized by some degree of genomic instability, which acts as a driving force in both cancer development and progression. This course will discuss the mechanisms that lead to genomic instability, different ways the genome may be altered, how genomic changes alter cellular function, and the role that genomic instability plays in cancer therapeutics.

1 credit

2 credits

Research.

1 credit

Thesis.

1 credit

Biometry

This course introduces basic applied descriptive and inferential statistics. Topics include elementary probability concepts, an introduction to statistical distributions, point and interval estimation, hypothesis testing, and simple linear regression and correlation. Basic data management and analysis techniques will be introduced using appropriate statistical software packages. Prerequisites: College Algebra & at least one course in Calculus.

3 credits

The objective of this course is to provide basic and intermediate skills necessary to apply regression methods to clinical and basic science research data. Topics include regression issues such as least squares estimation, hypothesis testing, diagnostics, model building and variable selection, and indicator variables. Simple and multiple linear regression, logistic regression, Poisson regression, and modeling of time-to-event (survival) data will be covered. The course uses a problem-based approach and applications to clinical and basic science problems are provided. Prerequisites: BMTRY 700.

4 credits

The course covers a variety of intermediate level topics required to complete core competencies for analysis and interpretation of clinical and basic science data. The course emphasizes experimental designs employed in biological and medical research, including randomized block and nested designs, and factorial experiments. Longitudinal data methods including random and mixed effects models, and missing data methods are covered.

4 credits

This course covers levels of measurements, order statistics, statistical methods for independent and correlated samples, distribution-free measures of association and testing. Students will identify situations where parametric techniques do not apply; to apply nonparametric methods for testing equality of variances; to test goodness of fit of data to a probability distribution; and to analyze one-and two-way layouts with nonparametric multiple comparisons.

3 credits

This course covers basic probability theory, random variables, transformation of random variables, expectation, moments and moment generating functions, discrete and continuous probability distribution functions; joint, marginal, and conditional distribution functions, bivariate normal distribution, and inequalities.

3 credits

This course is the continuation of Theoretical Foundations of Statistics I. Topics covered are order statistics, stochastic convergence, point and interval estimation, hypothesis testing, evaluation of estimates and tests, and asymptotic theory. Prerequisites: BMTRY 700, 706.

3 credits

This course offers a short review of standard measures of association and chi-square methods for binomial and multinomial distributions, followed by several special-purpose two-dimensional techniques. Other areas covered include the development of maximum likelihood-based inference (unconditional and conditional) for categorical data using generalized linear models. Models for binomial, multinomial and count data will be examined. In addition, topics including log-linear models, analysis of three-dimensional and higher tables, model selection strategies, regression model diagnostics, analysis of correlated or matched data, and generalized estimating equations, will be covered. Prerequisites: BMTRY 700, 701, 706.

3 credits

This course provides an overview of infectious disease epidemiology with an emphasis on the application of epidemiologic techniques to a variety of diseases. Lectures, supplemented by video presentations and case studies provide the framework for the course.

3 credits

The matrix representation of the general linear statistical model is studied through the implication, distribution, and partitioning of quadratic forms and their probability distributions. Estimation of parameters in the linear model by methods of maximum likelihood and least squares will be presented along with the accuracy and precision of these estimators. Estimability in both the full rank and less than full rank models is introduced. The test statistic for the general linear hypothesis is derived, and its distribution is determined under an assumption of normally distributed errors for both the null and a general alternative hypothesis. Sufficient examples are given to show its application to tests on means as well as in ANOVA and ANOCOVA. Students prepared in basic statistical methods and theory, and matrix algebra are eligible to take this course. Prerequisites: BMTRY 700, 706, 707, 710.

3 credits

This course is intended mainly for MS and PhD Students in DBE interested in the statistical methods and issues arising in a variety of clinical trials. The course will include topics in adaptive/flexible study design, adaptive randomization, sample size estimation, missing data handling, interim analysis methods, and issues in data analysis. The course will also cover topics related to the statistician's role in clinical trials, including the presentation of statistical information and statistical monitoring of safety data. At the completion of the course, students will have the tools to collaborate with clinicians in the design and implementation of clinical trials as well as analysis of study data, and will have developed their skills in being more critical readers of the medical literature.

2 credits

It is a graduate course on effective and sophisticated approaches to Bayesian modeling and computation in biostatistics and related fields. The course begins with a gentle introduction of Bayesian inference starting from first principle, but it intends to cover the philosophical backgrounds, logical developments and computational tools associated with Bayesian. Prerequisites: 700, 706, 707, 710.

3 credits

Required for all students with emphasis in biostatistics and epidemiology prior to obtaining a masters degree. Teaches students how to participate in collaborative research including methods for sample size estimation, preparation of plans for statistical analysis and of analytic reports. Those students in the Ph.D. program who do not have previous collaborative working experience and/or training would also be required to take this course.

2 credits

This is an introductory course in theory and application of analytic methods for time-to-event data. The methods covered include nonparametric, parametric, and semi-parametric (Cox model) approaches. The topics covered will also include types of censoring and truncation, sample size and power estimation, and a brief introduction to counting process method. Extensive use of SAS procedures for survival analysis is incorporated into the course. Prerequisites: BMTRY 700, 706, 710, and working knowledge of SAS.

3 credits

This is a comprehensive course providing an overview in the design and conduct of clinical trials. The course covers the types of clinical trials; study design (including sample size estimation); randomization methods and implementation; project and data management; ethics; and issues in data analysis (e.g., intent-to-treat; handling of missing data; interim analyses). The course is designed primarily for the students in the Department of Biostatistics, Bioinformatics, and Epidemiology; however, both clinical and basic science investigators can benefit from this course provided they have the required background in basic statistics. Prerequisites: BMTRY 700.

3 credits

This course is required for participants in the Clinical Masters program and Ph.D. students in the Department of Biometry and Epidemiology. The objective of the course is to prepare a grant application (R03, F31, K-award, etc.) for submission to a funding agency. Students learn grantsmanship, develop the sections of a grant (aims, background, preliminary studies, and methods), learn about IRB regulations and procedures, about ethics, and develop an IRB application. They also develop a research budget. Students will be given examples of successful grants and grants that have not been funded to discuss. Students should come to the course with a research idea that can be developed into a grant and, if possible, with preliminary data. Prerequisites: 700, 710, 736 or permission of instructor.

2 credits

This course will consist of multivariate techniques in biology and medicine including multivariate tests of mean vectors and covariance matrices, multivariate analysis of variance and regression, repeated measures analysis, random and mixed effects models, generalized estimating equations, generalized linear mixed models, canonical correlation, factor analysis, principal components analysis, discriminant analysis. Directed to biostatistics students; useful for epidemiology students. Prerequisites: BMTRY 702, 706, 710, Knowledge of Matrix Algebra & SAS.

3 credits

This survey course will introduce students to the major cancer risk factors. For the major cancers the most important epidemiological studies will be reviewed. The issue of genetic susceptibility and the use of biomarkers in cancer epidemiology will be studied as well as cancer screening.

3 credits

This course provides an introduction to basic epidemiologic principles including measurements of disease occurrence, study designs (cohort, case-control, randomized clinical trials) and calculation of risk. Lecture material is supplemented with exercises and discussion of examples from the epidemiologic literature and presentations of epidemiologic studies by guest speakers. Prerequisites: None. (Required MS and PhD).

3 credits

This is an advanced course designed to acquaint students with the use of epidemiology in the study and investigation of cardiovascular diseases. Prerequisites: BMTRY 736 or permission of instructor.

3 credits

An emphasis will be placed on procedures used in the implementation of epidemiological research studies. Prerequisites: BMTRY 736 or permission of instructor.

3 credits

The course gives a comprehensive entry-level introduction to bioinformatics. It covers a wide variety of topics in bioinformatics, including sequence analysis, protein structure prediction, gene prediction, genome analysis, proteomics data analysis, database, transcription profiling, etc. This course is designed to provide a broad foundation in bioinformatics for advanced courses. A biology background is helpful, but not essential for this class. Students without a biology background may wish to attend one or two sessions reviewing biology outside the class, which are currently provided by the instructor.

2 credits

The field of Environmental Epidemiology encompasses the investigation of environmental factors and how they affect human health. Environmental epidemiologists study health effects in populations resulting from exposure to physical, chemical, and biological agents. This includes the contribution of social, economic, and cultural factors that are related to these exposures. Occupational Epidemiology provides an introduction to clinical and epidemiologic aspects of occupational health and recognition and prevention of occupational diseases and injury. Case study approaches are used to learn about epidemiologic applications to occupational health. This course helps to address some of the 15 learning competencies of the doctoral program in Epidemiology and is intended for advanced epidemiology students to become familiar with applications of epidemiology to environmental and occupational problems.

3 credits

This course will provide a comprehensive and quantitative view of the design, conduct, analysis, and interpretation of epidemiological studies and use of EGRET software. There is a more in-depth coverage of topics than in Epi I. Prerequisites: BMTRY 700, 710 concurrently.

3 credits

This course will provide an in-depth quantitative view of advanced statistical analysis of epidemiological studies. The use of epidemiological analysis software (Epicure) will be taught. Builds on techniques developed in Epi II. Prerequisites: BMTRY 700, 710, 747.

3 credits

This course introduces students to the principles and practices of molecular epidemiology and provides an overview of the application of biologic markers of exposure, disease or susceptibility to epidemiologic investigations of exposure-disease relationships. Students will be guided through general principles that draw on issues of validity and reliability, technical variability and control, biologic specimen banks with a strong emphasis on study design and how to incorporate biomarker studies into epidemiology practice.

3 credits

The need for a public health workforce trained in equity-based approaches to social determinants of health has increased and is driven by a significant body of literature. In this course, students will learn principles and concepts of health equity and social determinants of health and relevant models and methodological issues in social epidemiologic research.

3 credits

This course introduces students to the analysis of longitudinal data collected on individuals over time. Topics will include linear models for panel data, restricted maximum likelihood, choice of covariance structure, linear and generalized linear mixed effects models, marginal models and GEE, penalized quasi-likelihood, missing data and dropout. Pre-requisites: Biometry 701, Biometry 707.

3 credits

This course focuses on the basic epidemiological and statistical issues to be found in the study of the spatial/geographical distribution of disease. The topics of disease mapping, disease clustering and ecological analysis will be examined.

3 credits

Students learn to use the primary statistical software packages (SAS, R, Stata), principles of data management, and scientific document preparation.

3 credits

Examination of chronic disease from an epidemiologic perspective, with an emphasis on methodological and practical issues of study designs, exposure and outcome assessment, factors determining the distribution of selected chronic diseases and critical review of relevant epidemiologic literature. Students are introduced to disease registries, their purpose, benefits and limitations.

3 credits

The objectives of the Methods and Outcomes in Cancer Population Sciences is to increase the knowledge and skills of early stage clinicians and basic science researchers in conducting patient oriented and translational cancer research.

3 credits

Public Health Seminar is a required course for Biostatistics and Epidemiology PhD and MS students in the Department of Public Health Sciences (DPHS), to be completed in the fall and spring semesters of the student's first year in the program. Students attend DPHS-sponsored seminars every other Monday throughout the semester to gain exposure to contemporary research topics in biostatistics and epidemiology. Seminar speakers are invited guests to the department and represent a diversity of research topics that are complementary to the research interest of DPHS faculty. On alternating Mondays, the department sponsors its own Brown Bag seminar series featuring research presentations by DPHS faculty and advanced students actively engaged in mentored projects. This valuable exposure helps first-year students identify potential mentors and projects for summer research hours, as well as possible dissertation advisors and research topics.

1 credit

In this 14-week, 15-credit hour course compromising six modules, students will receive didactic instruction, one hour per day, from national leaders in cancer research who collectively will present state-of-the-art cancer information across multiple perspectives - basic sciences, clinical sciences, and population sciences with an emphasis on disparate outcomes in breast, prostate, head/neck, and cervical cancer. Students will also spend 30 hours per week working in the research laboratories/offices of their mentors.

4 credits

This course is intended for Ph.D. students in Biostatistics. The course will begin with a review of basic mathematical concepts: probability and measure, integration, modes of convergence. A decision theoretical approach to statistical inference will be introduced. In statistical estimation theory, topics such as families of distributions, point estimation, unbiasedness, algorithmic issues, etc. will be included. In hypothesis testing the Neyman-Pearson theory, unbiased tests, permutation tests, and likelihood based tests will be discussed in depth. In asymptotics, limit theorems, relative efficiency, Wald's statistic, Rao's score statistic, etc., will be discussed. An overview of robust statistical procedures will be provided. Prerequisite: BMTRY 707 Theoretical Foundations of Statistics II (3).

4 credits

Lectures will cover the following areas in oncology research: (1) clinical and statistical design of phase I, II and III trials; (2) incorporation of correlative and biomarkers in clinical trials, (3) considerations in chemotherapy, surgery, radiation and multimodality trials, (4) quality of life and other patient reported outcomes in cancer research, (5) the protocol review and IRB process, (6) informed consent, (7) data collection, trial monitoring and investigator responsibilities, (8) the grants process and mentoring. In addition to the didactic portions of the training, each trainee will have a clinical research proposal which will be developed into a letter of intent (LOI) for a clinical trial. Other contact hours will take the form of a journal club where clinical research papers from journals such as Clinical Cancer Research or Journal of Clinical Oncology are discussed, and protocols that are being undertaken at HCC are reviewed and discussed. Students will be required to attend and take part in the HCC Protocol Review Committee's monthly meetings.

2 credits

This course will provide a survey of bioinformatics research areas and statistical methods needed to analyze data in these areas. This course will introduce students to biological concepts and statistical problems in various bioinformatics research areas, including functional genomics and cancer genomics. Statistical methods, such as multiple testing, clustering, classification, and high dimensional data analysis, will be discussed to address statistical problems in these research areas. Freeware and online resources related to these topics will be explored.

2 credits

This course is intended for biostatistics MPH and Epidemiology PhD and MS students interested in applied statistical methods for analysis of categorical and correlated data. The categorical data analysis sessions include methods for stratified 2x2 and r x c contingency table data, ordinal data, matched pair dichotomous data, and count data. The correlated data analysis section covers random and mixed effects models and generalized linear mixed models. The didactic classes are augmented by SAS and R sessions led by the TA's. At the completion of this course, students will have the tools to analyze these data using SAS and R, and make appropriate inferences from the analyses. Prerequisites: BMTRY 700, BMTRY 701 and Probability and Statistical Inference.

3 credits

This one-semester course provides an introduction to fundamental principles of probability and inference including: laws of probability, discrete and continuous random variables and their probability distributions, select multivariate probability distributions, sampling distributions and the central limit theorem, point and interval estimation including maximum likelihood, an overview of the hypothesis testing framework, and common hypothesis tests including the likelihood ratio, Wald, and score tests. Prerequisites: At least one semester of Calculus.

3 credits

Special Topics in Biostatistics, Bioinformatics, and Epidemiology.

1 credit

Machine learning is the interdisciplinary field at the intersection of statistics and computer science which develops such statistical models and interweaves them with computer algorithms. This course provides an introduction to the theory with a basis in real-world application, focusing on statistical and computational aspects of data analysis. It is designed to serve as an introduction to the fundamental concepts, techniques and algorithms of machine learning. The course will cover following topics: data representation, feature extraction, dimension reduction, supervised and unsupervised classification, support vector machines, latent variable models and clustering, and model selection. During the course of discussion, a main thread of probabilistic models will be used to integrate different statistical learning and inference techniques, including MLE, Bayesian parameter estimation, information-theory-based learning, EM algorithm, and variational methods. Prerequisites: BMTRY 706, BMTRY 701/702.

3 credits

Research.

1 credit

Research.

1 credit

Research.

1 credit

Research.

1 credit

Research.

1 credit

Research.

1 credit

Research.

1 credit

Research.

1 credit

Research.

1 credit

Research.

1 credit

Research.

1 credit

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1 credit

Research.

1 credit

Research.

1 credit

Research.

1 credit

Basic Sciences

This is an online course in histology that involves learning the microscopic architecture and function of cells, tissues and organs of the human body. The course content is presented via interactive lectures and virtual labs. A unique feature of this course is the use of virtual microscopy to examine specimens over the Internet using a browser interface instead of a microscope. Learning is facilitated by practice quizzes and assessed by both open- and closed-book examinations.

4 credits

BremMethod.com provides synchronous, live, small group, online MCAP prep courses for institutions. They specialize in working with institutions to provide their 6-week course as an integrated part of a program curriculum. The BremMethod assesses student engagement using a standard rubric and will share performance with the program director throughout the course. The course grade will be determined by rubric.

2.5 credits

This intensive gross anatomy course is designed to prepare students for entry in the field of health professions in general, with a focus and emphasis on medical and dental curricula in particular. The course provides students with a detailed examination of all structural aspects of the human body with a special emphasis on the anatomy and anatomical relationships significant to common clinical medicine topics and surgical procedures. It is presented by regions through lectures and matching online laboratories. The material is organized in units and presented in a logical fasion, i.e. Superficial Back and Upper Limb, Thorax, Abdomen and Pelvis, Lower Limb and finally Head and Neck. Throughout the course, imaging techniques including CT scans and x-ray radiography are used to introduce the student to the clinician's perspective. The course content is also designed to correlate with important clinical problems that students may encounter as practitioners, and additional reading assignments are included in the material to be studied by the students. The students also have the opportunity to further their knowledge of anatomy by using online resources that will be made available to them through a course management system.

4 credits

A live, online, MCAT prep course that goes beyond memorization; teaching mastery, strategy, and mental resilience. The course framework blends content expertise with performance psychology, helping students improve retention, test-taking skills, and overall confidence. Group sessions are augmented with a large question bank subscription and individual coach. Grades are awarded based on assignment performance and full-length MCAT practice exams.

3 credits

Students attend weekly 1 hour workshops led by various faculty members on writing personal statement for applications, writing resumes and CVs, interviewing skills, professional etiquette, and how to get the most out of clinical shadowing experiences. Mock interviews and critiques of draft resumes and personal statements will be provided. Students will develop a professional portfolio that can serve as the basis for applications to professional schools. Assessment will be based on attendance and participation.

1 credit

Each student attends a minimum of 10 Grand Rounds seminars over the semester, from at least three different disciplines (e.g. Medicine, Surgery, Pediatrics, Psychiatry). For each Grand Rounds attended, the student must submit an original 1 page report describing what they learned for credit. The reports and course are graded pass/fail/honors.

1 credit

Students will have the opportunity to shadow a physician working in the MUSC Emergency Department and/or the autopsy service. The students will attend the clinic during the semester and write up the history of the patient and the diagnosis and treatment plan.

1 credit

This course offers a focused and accelerated review of the core vocabulary and foundational skills that are crucial for success in graduate-level studies across both the natural and social sciences. Students will revisit and strengthen their understanding of fundamental terminology and concepts. Common scientific calculations are practiced and mastered without the use of a calculator, helping students to develop mental calculation abilities and confidence in handling quantitative tasks. In addition, there is a strong emphasis on reading techniques to help students efficiently process and understand complex material, which is essential for graduate-level academic success. Grades are determined by a combination of quiz and exam scores.

2 credits

This course introduces the basic and clinical concepts of immunology, with an emphasis on oral biology. Students who pass this course will understand how the immune system works in health, and how its dysfunction causes or contributes to disease. Topics covered in this course include fundamentals of adaptive and innate immunity, immune regulation, immunization, and transplantation biology and tumor immunology. Disorders such as hypersensitivity, graft rejection, graft-versus-host disease, and autoimmunity are introduced as well as the drugs used to treat these diseases. The impact of the human microbiome on health and disease is also discussed. The course grade is based on four written exams and evidence-based medicine assignment.

4 credits

This course will foster a knowledge base and understanding of the fundamentals of bacterial physiology and genetics; clinical bacteriology, virology, parasitology and mycology; antimicrobial therapy; and infection control. The primary goals of the course are to explore the relationship between the physiology of medically important microbes to the pathobiological sequelae of human-microbial interactions, with particular reference to the role of microbes in human disease. In addition to lecture, instruction includes problem based, small group exercises in microbiology with clinical case scenarios. The course grade will be based on 3 multiple choice question exams, in-class quizzes, small group laboratory write-ups, and an evidence based decision making paper (PICO) on an infectious disease question of their choice.

4 credits

This course introduces pre-professional students to the analytical methods, resources, and approaches to quality improvement analyses in health care using a realistic case-based study.

2 credits

The course presents core concepts of biochemistry and molecular biology to pre-medical and pre-dental Master's program students. It is divided into four separate modules. In the first module, basic principles of biochemistry will be introduced. In the second and third modules, an in-depth discussion of key metabolic pathways will be presented. Finally, the fourth module will cover essential aspects of molecular biology and advances in biotechnology.

3 credits

Caring for the Community. Caring for the Community is an interprofessional course aimed at exposing students to the social and financial resources available within the Charleston area to our patients, in particular those who are uninsured or under-insured. Discussions, debates, panels and small group activities will serve to increase our knowledge as providers, and to better serve our patient population in regard to addressing all of their needs, beyond medical needs only. Topics addressed will include health disparities, population health and cultural factors affecting delivery of healthcare, social determinants of health and community resources. All students enrolled in IP-700 will preferentially be afforded opportunities to rotate through the CARES medical clinic as well as shadowing opportunities in the CARES PT/OT clinic, the ECCO Dental clinics and joint Low Country Food bank-CARES clinic events.

2 credits

Research

1 credit

Cancer Biology

This course introduces students to transformative technologies shaping the future of cancer research and treatment, emphasizing the shift toward precision oncology. Topics include the application of artificial intelligence and machine learning algorithms in genomics and pathology, exploring liquid biopsy techniques for non-invasive cancer detection and monitoring, and the development of next-generation therapies, including mRNA-based cancer vaccines, antibody-drug conjugates, and personalized CAR-T cell therapy. This course includes three didactic lectures a week for 2 hours over 15 weeks. Assessment will be based on three in-class exams at the end of each 5-week period.

2 credits

This second-year elective course introduces principles and methods for reducing cancer incidence and mortality through evidence-based prevention and control strategies. Topics include behavioral, environmental, and biological risk factors; screening and early detection; health disparities; and population-level interventions. Students will examine epidemiologic data, evaluate prevention programs, and explore translational approaches linking public health research to clinical practice. Emphasis is placed on interdisciplinary collaboration and application of cancer prevention science in biomedical contexts. This course will meet twice a week for 90 minutes over 5 weeks. Each week includes a didactic lecture that lays the foundation for discussion of a related journal article in the following class. Assessment will be based on participation in journal discussion, and a final project that includes a presentation and generation of an NIH-style specific aims page.

1 credit

This weekly seminar provides a forum for graduate students in the Cancer Biology program to present and discuss progress on their thesis research . Each session features student-led presentations of experimental findings, critical evaluation of methodologies, and group discussion of emerging trends in cancer biology. The series fosters scientific communication skills, peer feedback, and collaborative problem-solving in a supportive research environment. Assessment will be based on students’ demonstrated ability in their led discussions to critically evaluate the research, effectively lead group discussion, and professional presentation of their research project.

1 credit

2 credits

The basic "Hallmarks of Cancer" defined as sustained proliferative signaling, evasion of growth suppressors, resisting cell death, avoiding immune destruction, enabling immortality, invasion and metastasis, and deregulation of cellular energetics are all driven by protein-to-protein signaling. This course will discuss broad discoveries that have shaped the field of cancer cell signaling and provide an overview for how these signaling processes pertain to modern cancer research. This course is offered to students that have successfully passed first year courses.

1 credit

Current and emerging topics in celluar signaling will be presented and discussed in a journal club-style format. Students will present topics related to cellular signaling using faculty-approved articles from peer-reviewed journals, and will be expected to actively participate in the discussion with other students, post-doctoral fellows and faculty members.

1 credit

This is a specialized course in the field of redox biology. Organized by MUSC with the support of the SC COBRE program, the course also features faculty and students from the Karolinska Institute, Stockholm, Sweden, and the University of Nebraska-Lincoln Medical Center.

2 credits

Course Description This course presents key concepts in glycobiology and its role in human medicine. This includes the biological roles of glycosylation, glycan biosynthesis, glycan analysis techniques, and glycans in disease and medicine. The first portion of the course will focus on basic science and then transition into translational applications of glycobiology. The various topics will be presented in a combination of lecture, journal club, and discussion formats. Specific learning objectives for the course are as follows: - Be able to describe the basic language of glycobiology and functions of glycans in humans - Understand the importance of N-glycosylation in protein folding - Understand the role of dynamic O-GlcNAc modulation in cell signaling - Relate genetics to glycan diversity - Outline how glycans participate in the various hallmarks of cancer - Explain the role of glycan-binding proteins in immune responses - Be able to describe current approaches for analysis of glycans in the laboratory - Understand the application of glycomics to clinical assays and therapeutics - Discuss and present current literature on biomedical applications of glycobiology

1 credit

Research.

1 credit

Thesis.

1 credit

Graduate Studies

This course provides a descriptive and inferential statistics commonly used in biomedical research. Topics include elementary probability theory, and introduction to statistical distributions, point and interval estimation, hypothesis testing, regression and correlations. The course is intended for graduate students in the basic and clinical sciences, clinical residents/fellows, and medical and dental students who seek a working knowledge of biostatistical methods and their applications.

4 credits

This course prepares participants to coordinate cost-effective health care research which protects the rights and safety of human subjects. The course is offered on-line and is required of all TL1 trainees. TL1 trainees will be required to take the course sometime during their first year in the program.

1 credit

Trainees will present a clinical case that will be followed by a research discussion by a physician-scientist.

1 credit

First Year Curriculum Ph.D. students are required to enroll in three 9 week laboratory rotations spanning the Fall and Spring semesters. All students will rotate through three different laboratories to maximize their exposure to a diversity of mentors, scientific experiences and technologies. Students are urged to attend the seminars and journal clubs of the program in which they are participating in order to get a better sense of where they might be most comfortable during their thesis work.

1 credit

This course provides exposure to cutting edge cancer research using a combination of seminars and journal club discussions. Seminars will feature presentations on the latest discoveries by leading experts from around the country. Journal clubs will provide an in-depth discussion of research articles that relate to each seminar topic or highlight other important advances in cancer research.

1 credit

The primary objective of this class is to provide an opportunity for graduate and post-graduate students to learn basic teaching and evaluation techniques, as well as presentation skills. Whether presenting research at national or international meetings, or teaching in a formal classroom, teaching and presentation skills are necessary for most professional careers.

1 credit

This course is designed for pre and postdoctoral trainees and K scholars to learn the principles of rigor and reproducibility in research design and methodology.

1 credit

Part one of this course series aims to develop essential competencies for biomedical scientists, including communication, time management, and career planning, with a strong emphasis on developing skills to be successful as a graduate student. Students will also learn advanced strategies for reading, interpreting, and presenting scientific literature. Through interactive workshops, peer feedback, and real-world case studies, participants will gain confidence in professional development and scholarly engagement.

2 credits

Part two of this course series aims to develop essential competencies for biomedical scientists, including communication, networking, and career planning, with a strong emphasis on developing skills to identify and navigate future career paths. Students will also continue to develop advanced strategies for reading, interpreting, and presenting scientific literature. Through interactive workshops, peer feedback, and real-world case studies, participants will gain confidence in professional development and scholarly engagement.

2 credits

1 credit

The 4 sessions of this 1 credit hour course consists of graduate-level, interactive seminars that equip biomedical scientists with the knowledge and skills to conduct research responsibly. The course emphasizes ethical principles, policy awareness, and real-world decision-making in areas NIH highlights: data integrity, authorship, human/animal subjects, conflict of interest, mentorship, collaborations, peer review, safety, and inclusive environments.

1 credit

MUSC graduate students (max 3/term per location) will intern with the College of Charleston Program in Neuroscience (coordinator: Dr. McGinty) or the Citadel Dept of Biology faculty (coordinator: Dr. Bacro) over a full semester. The student will need to fill out the CGS 745 application form to be placed as an intern at one of the two locations. Unless agreed otherwise with their coordinators, students will commit to attend the lectures/laboratories and prepare up to 2 student directed sessions. Students will plan, execute, and evaluate each session, and will prepare at the end of the course a reflective report and a plan for the next term's syllabus.

2 credits

This course give students an understanding of the role that stem cells have in human health and disease. This includes the roles and applications of pluripotent stem cells and adult stems in development and disease. Students will obtain detailed knowledge on the different ways that stem cells are utilized by our body naturally as well as how one can harness the power of stem cells in regeneration of tissues as well as in the treatment of diseases. During the course, students will also gain an understanding for the various techniques and current methodology used for the analysis of stem cell function. Additionally, students will learn about potential ethical issues regarding stem cell usage. Prerequisite: 1st year core curriculum.

1 credit

Acquaints students with an area of specialized research currently under investigation in a faculty member's laboratory. This course is for students enrolled in the summer undergraduate research program.

0.5 credit

This course will assist Master's in Biomedical Sciences students in writing their research proposal and/or thesis in the summer between their first and second year. The course is designed to synthesize the knowledge and skills developed in research courses and apply them to the masters thesis process. Students learn about all aspects of the process of developing and carrying out masters thesis, and they gain an understanding of standards and expectations that students need to meet to be successful in completing the thesis writing process. Throughout the course, students are required to work closely with their major advisors, and committee as appropriate. The course will be taught in a seminar style with extensive dialogue among the students and instructors.

1 credit

R is quickly becoming the most widely used programming language in the biomedical sciences. This course is designed as a self-guided, introductory coding course that offers students the opportunity to learn the basic principles of Coding in R. Students will complete a curated portfolio of online modules (Offered by DataCamp) interspersed with in-person group activties. The online modules are laid out two sections: 1) Introduction to R, with an emphasis on writing efficient code, and 2) Streamlining script processing and generating visuals.

2 credits

This nine-week, interdisciplinary course prepares students to move their ideas persuasively from pipette to pen. Students encounter a variety of scholarship on science and persuasion, focusing on the fundamentals of audience (who you write for), genre (what patterns you write from), and style (how you work with words), and develop rhetorical competencies for both professional and public contexts. To these ends, the instructors deploy an array of teaching techniques that include interactive lectures, group discussions, on-the-spot quizzes, and small-scale team projects. Each week, students can reasonably expect to write between 500 and 1,500 words outside of class. That number may vary according to the assignment and the instructor.

1 credit

The 18 sessions of this 5-week, 3 credit hour course present fundamental principles of protein structure and function. Proteins, the most abundant and diverse family of macromolecules within the cell, play a myriad of essential catalytic and structural roles within the cell. They undergo multiple post-translational modifications and interact with numerous partners, including other proteins, RNA, DNA and membranes. These topics will be considered within the context of health and disease, with an emphasis on the molecular mechanisms underlying fundamental cellular processes and underscoring the impact of mutant proteins on cell behavior and the importance of proteins as therapeutic targets.

1 credit

The 25 sessions of this 7-week, 4 credit hour course present the fundamental principles of inheritance, maintenance and expression of the genetic material. The first 6 sessions focus on the principles and practice of classical and molecular genetics, and the next 7 focus on the replication, repair and transmission of the DNA genome within the context of the mammalian mitotic and meiotic cell cycles. The final 11 sessions focus on the expression of the genome, incorporating discussions of transcription, epigenetic modifications of DNA and histones, nucleolus and rRNA synthesis and maturation, mRNA processing, nuclear export and translation, and regulation by non-coding RNAs.

2 credits

The 18 sessions of this 5-week, 3 credit hour course address the fundamental principles of cell structure, compartmentalization, and function. The first 10 sessions focus on the structure, function and dynamics of the endomembrane systems of the cell, the cytoskeleton, major organelles and programmed cell death. The final 7 sessions address cell:cell and cell:matrix interactions and the complex process of signal transduction. The overarching principles involved in the process of signal transduction, which most often involves the transduction of a signal from an extracellular ligand to a nuclear response, will bring together the principles discussed in the initial part of this course and those discussed in modules I and II.

2 credits

This course highlights essential tools and approaches required to achieve a high level of competency in biomedical research. Students will be exposed to the practical 'nuts and bolts' of a wide variety of molecular biology approaches spanning established basics, and timely new techniques. Course material will complement and align with scientific concepts covered in the Core Curriculum. This training is expected to provide students with foundational knowledge and an invaluable toolkit that will robustly enhance their ability to achieve scientific success. This course is for all incoming first year graduate and MS students in the Biomedical Sciences Program.

2 credits

This course is designed to provide students with an overview for creating and submitting an F31/F31 Ruth L. Kirschstein Predoctoral Individual National Research Service Award (NRSA) to the National Instititues of Health (NIH). Students will discuss the componenets of the gratn proposal and learn how to develop a competitive grant application. Throughout the course, students will have an opportunity to draft several componenets of the grant application and receive feedback from faculty.

2 credits

This semester long course introduces graduate students to essential concepts in the practice of biomedical science, such as critical thinking, responsible conduct of research, reproducibility, transparency and rigor in science, and professional development. The course utilizes didactic lectures, group activities based on hypothesis development, student discussion of relevant literature, analysis of most appropriate funding mechanisms, and a range of skills focused on optimal development of career options..

2 credits

The new LFTL (Learning from the Literature) course is required for 1st year PhD students in the Biomedical Sciences. The course is focused on helping students make the transition to "learning from the literature". A discussion of what the literature is and how to access it, an understanding of how to read scientific papers, and practice in thinking critically about the hypotheses being tested, experimental design and data presented are central to the course. The students will work individually and in groups and have multiple opportunities for discussion and presentation..

2 credits

This course presents key concepts in glycobiology and its role in human medicine. This includes the biological roles of glycosylation, glycan biosynthesis, glycan analysis techniques, and glycans in disease and medicine. The first portion of the course will focus on basic science and then transition into translational applications of glycobiology. The various topics will be presented in a combination of lecture, journal club, and discussion formats. Specific learning objectives for the course are as follows: - Be able to describe the basic language of glycobiology and functions of glycans in humans - Understand the importance of N-glycosylation in protein folding - Understand the role of dynamic O-GlcNAc modulation in cell signaling - Relate genetics to glycan diversity - Outline how glycans participate in the various hallmarks of cancer - Explain the role of glycan-binding proteins in immune responses - Be able to describe current approaches for analysis of glycans in the laboratory - Understand the application of glycomics to clinical assays and therapeutics - Discuss and present current literature on biomedical applications of glycobiology

1 credit

This comprised of various sections, each of which represents a 2 credit "mini-course" that meets three times per week for 5 weeks. The courses are scheduled during 3 sequential blocks in the spring semester, and there are at least two choices per block. These courses are small and interactive, usually involving lectures, group discussions, and presentation of the primary literature. The courses address important topics in contemporary biomedical science that go beyond the foundational material covered in the Fall core curriculum taken by first year students.

2 credits

This is a slice course that will be given in the fall semester. This merit graded class will give students an understanding of an array of digestive diseases including liver fibrosis, nonalcoholic fatty liver diesase, hepatitis C virus infection, heptocelluar carcimoma, mircobial host interactions. Students will obtain an understanding of current methodologies and techniques used to study theses diseases.

1 credit

This course introduces TL1 trainees to translational research via discussion of papers that exemplify translational research. The Journal Club meets once a week at a time to be determined based on the schedules of the trainees and course facilitators. The journal club is limited to a 1 hour discussion. Trainees are organized into teams, usually 3 members/team. The team chooses 3 scientific papers that are representative of a basic science discovery that ultimately led to a new therapeutic approach. The first paper presented is the basic science paper that serves as the underpinnings of the next two presentations, that represent the clinical validation of the new therapeutic approach and its dissemination and implementation. Each week a trainee will be responsible for the research paper and leading the discussion along with the faculty mentor. The faculty mentor, program director or associate program director serve as facilitators. The trainee's mentor serves as an advisor to the trainee prior to the meeting of the journal club. The trainees are required to answer 3 questions after the 3 presentations that relate to the scientific merit of the papers and translation to a new therapeutic approach. The Journal Club is graded honors/pass/no pass.

1 credit

Reproducibility is the cornerstone of scientific research. Replication of your own or someone else's research provides the momentum for forward progress within the scientific community. In In recent years, there has been increased attention on an alarmingly pervasive state of irreproducibility among published scientific studies, with a number of high-profile cases resulting in potential harm to human subjects and censure of investigators. This course is designed; 1) familiarize students with sources of irreproducibility across the spectrum of scientific research, 2) provide essential principles for the design and conduct of rigorous research and its transparent reporting 3) develop effective writing skills, 4)understanding the peer review process, and 5)develop a rigorous NRSA application.

2 credits

The MSTP Seminar is a mandatory requirement for all MSTP students throughout their training. It is held on the 2nd and 4th Monday of each month at 4:00-5:00 pm. The series encompasses several types of presentations. 1.Speakers who are either mentors, former mentors, or potential mentors for the students 2. Senior students present their project from the Research Nexus rotation where they developed and wrote a clinical investigation protocol 3. On the second Monday of each month, a senior student presents a clinical case in a disease area in which they are interested. The case presentation lasts roughly 5 to 10 minutes. Following the case presentation, a physician scientist discusses the case from a clinical and research perspective. Students get a chance to see the case discussed from a more scientific approach compared to what they might see on the wards or in the clinics. They are able to see how one can bring science to bear on the understanding of pathophysiologic processes and the development of new therapeutic approaches. 4. During the spring semester, additional dates are set aside for the more senior Ph.D. students to present their research as a practice for their dissertation defense.

0.5 credit

Modem data formats, publicly available analytical tools, visualization methods, statistical design, and experimental strategies related to biological "Big Data" will be presented in lecture format each session. A tool of particularly useful character will be demonstrated through an in-class tutorial each session. Students must participate in the tutorial for course credit, and an in-class laptop with Microsoft Office and Google Chrome installed is required for each student. Practical use of the tools and strategies will be experienced by students through homework assignments designed to independently apply presented lecture and tutorial material. Each following session will begin with students presenting in groups to the class specific parts of these assignments to create a story regarding their collective data analysis. This course is mandatory for T32 Cellular, Biochemical, and Molecular Sciences Training Program students.

1 credit

This course presents a wide variety of information in the broad area of drug discovery, including the early discovery phase (target development, in vitro and in vivo assay development, screening, lead optimization, structure-based drug discovery), mid-stage considerations (in vivo studies, ADME, toxicology and metabolism, advanced preclinical trials) and late stage discovery (clinical trials and marketing). The various phases of the drug discovery process will be introduced in the context of 3 successful drug discovery efforts, presented in a discussion format.

1 credit

Research.

1 credit

Research.

1 credit

Research.

1 credit

Research.

1 credit

Research.

1 credit

Research.

1 credit

Research.

1 credit

Research.

1 credit

Research.

1 credit

Research.

1 credit

Research.

1 credit

Research.

1 credit

Research.

1 credit

Drug Discovery & Biomedical Sciences

2 credits

This course will provide advanced knowledge on the mechanisms of cell responses to a wide range of environmental stresses including chemical, physical, anoxia/reperfusion and other pathogens. The course focuses on the signal transduction pathways leading to cell injury, carcinogenesis, necrosis, apoptosis, repair, regeneration, adaptation, and cytoprotection. We will cover the events at system, cellular and protection levels; however, emphasis is given to the interactions among intracellular signaling pathways. This course is useful for all biomedical students and, in particular, for students who completed the course "Cellular Defense Against Foriegn Chemicals" and want to continue their understanding of the effects of environmental stress at cellular and molecular levels.

4 credits

This hands-on course provides a solid introduction to the concepts and practical applications of light microscopy relevant to modern cell and molecular biology. Students will have opportunities for extensive hands-on experience with state-of-the-art equipment for optical imaging, digital image processing, and fluorescence and confocal/multiphoton microscopy guided by experienced academic and commercial faculty.

1 credit

This course covers advanced topics of medicinal chemistry related to the synthesis of complex organic molecules. Emphasis is on the strategy for stereochemical induction, functional group transformations, retrosynthetic analyses and catalytic reactions. The course involves didactic lectures and workshops targeted to synthetic design.

3 credits

Successful drug discovery research in both academia and the pharmaceutical industry is, by nature, a highly collaborative enterprise. Students who aspire to a career in drug discovery should be well-trained experts in their chosen area of research. However, they must also have an understanding of basic principles used routinely by collaborators in related research areas in drug discovery. Such knowledge will ensure that they can effectively communicate with scientists in other disciplines, and thereby facilitate the discovery of novel therapeutic agents. This course is the fourth in a series of four 8-week mini courses that form the core curriculum for the Department of Drug Discovery and Biomedical Sciences. The first 2 courses, DDBS 701 and 702, dealt with basic principles of pharmacology and medicinal chemistry in drug discovery, respectively. The third course in the core, DDBS 726, dealt with advanced concepts in medicinal chemistry. Taken together, these courses will cover all of the scientific principles that need to be understood for a career in drug discovery research. Specific learning objectives for DDBS 727 are as follows: Be able to design and describe experiments used to characterize receptor-active agents. Gain a working knowledge of proteomics and metabolomics. Be able to suggest experiments to explore the genetics of drug targets and drug action. Understand the dynamics of cell-cell interactions in normal and diseased cells. Understand the basic concepts in drug delivery formulation. Understand the role of aberrant signal transduction in disease, and be able to design experiments to measure signal transduction and protein-protein interactions. Be able to describe the issues surrounding the design and abuse of CNS-active agents. Be able to discuss literature examples of drug design in the CNS, endocrinology and antitumor areas. Gain an understanding of drug-related tocxicology and carcinogenesis.

2 credits

A general research and journal club seminar mandatory for all graduate students in pharmaceutical sciences. Guest speakers, faculty, and students participate. All. Faculty

1 credit

A variable credit course involving appropriate lectures, research-oriented laboratory work, written assignments and reports, and oral presentations.

1 credit

Research.

1 credit

Ethics

The course provides deep analysis of ethical issues in such areas as data management, international research, research ethics consultation, and relations between law and research ethics. It is an extension of the introductory materials covered in MCR 750.

2 credits

This online seminar series expands the range of educational opportunities for learners who are interested in clinical and translational research ethics (CTRE). It comprises hour-long asynchronous seminars each week with leaders from MUSC research administration, clinical/translational investigators, and invited guests from other institutions, each of whom focuses on the relation of their offices and activities to CTRE. Each seminar will be followed by small group discussions online. This course is part of the Clincal and Translational Research Ethics (CTRE) Fellowship Program; however Fellowship enrollment is not required. This course is available entirely online.

1 credit

Emphasis will be placed on the ethical issues associated with clinical research and practice. The class will focus on review of the competencies involved in the conduct of ethically responsible research. The process of assesing ethical issues in research and study will be described.The ethical considerations in study design and implementation, data management, data analysis, data interpretation and results presentation and publication will be described. As future educators, the students will be presented with the honor council process, assessment process for unethical classroom and study behavior, and the process for behavior modification and remediation. Ethical considerations in collaborative research will be presented. The course will consider evolving ethical issues in clinical research including human subjects and conflict of interest. The course will include a class participation system and will be available online.

1 credit

Special Topics course developed by student and mentor on a specific topic in their research area or grant topic.

1 credit

Microbiology & Immunology

This course represents an intensive and in-depth study of the areas of cellular immunology, immunogenetics, clinical immunology, and the immunobiology of tumor development. Each area will be presented with the intent of developing a sound understanding of experimental and theoretical observations. Emphasis will be placed on the most current research involving sophisticated methodology.

2 credits

Participation of graduate students in this course is mandatory. Guest speakers supplement the regular program. Each graduate student gives at least one seminar yearly.

1 credit

Initial lectures will review the fundamental principles of genetics. The principle focus of the course will be the genetics of human MHC and immunoglobulin allotypes. Major blood genes will also be discussed. Statistical methods employed in delineating the genetic contribution to human diseases will be reviewed.

1 credit

Course is a formalized, refereed journal club focused on topics of general interest in Microbiology and Immunology. Papers are limited to those published in high impact journals, e.g. Nature, Science or Cell, in the areas of microbiology and immunology. Students may choose their own papers, but the paper must be approved by two M&I faculty members. For each paper, two faculty members (chosen by the course director) will be designated as referees. The names of the referees will be publically announced, and the faculty and student referees will grade the presenting student. In this case, the student will get feedback from both faculty members and their peers. The student referees will also provide detailed written critiques of both the paper and the presentation. The referee system also assures that at least 5 people have read the paper.

1 credit

Research.

1 credit

Molecular & Cellular Biology & Pathobiology

MCBP Seminar Series. Students give a short seminar based on their own research to their peers and to their graduate committee members. Students are required to give at least two formal seminars during their training. The MCBP External Seminar Series invites leading scientists from the United States and foreign countries to present their work to both students and faculty in the MCBP Program. These seminars are on a broad range of topics representing each of the six divisions within the MCBP Program. Importantly, students have the opportunity to meet informally with the speakers over lunch.

1 credit

Current and emerging topics in craniofacial biology will be presented and discussed in a Journal Club style format. Initially, a faculty member will introduce and direct all students in the discussion of literature concerning oral-related research topics. Subsequently, students will present topics using faculty-approved papers from top-tiered journals. Students will be expected to participate in active class discussion with other graduate students, postdoctoral fellows, and faculty.

1 credit

In this series, students give a seminar based on their own research to their fellow students, advisory committee, faculty and post-doctoral fellows in the College of Dental Medicine. This is a great opportunity for the students to present their work in an informal setting and to receive constructive feedback on his/her studies from a large audience with different scientific backgrounds. Each graduate student will give at least one seminar yearly. Lectures will be supplemented with local as well as invited external speakers, whose research focus is on craniofacial biology.

1 credit

Current and emerging topics in marine organismal and environmental health will be presented and discussed in a journal club-style format. Students will be present topics related to the topic of marine organismal and environmental health using faculty-approved articles from peer-reviewed journals, and will be expected to actively participate in the discussion with other students, post doctoral fellows and faculty members.

1 credit

This course introduces students to methodology and theories involved in the study of cell biology and cancer through student participation in a formal journal club. The course will use student driven reviews of journal articles pertaining to cellular and molecular biology including basic mechanisms and cancer research. Students will be required to lead a discussion (2 hours) on at least one article which has been recently published on a broad range of topics including basic cellular mechanisms, cancer biology and disease. All students will be encouraged to ask questions and participate in discussions. Student presentations will be augmented by the addition of interested postdoctoral fellows and faculty.

1 credit

This is a journal club counting for 1 credit hour, which will meet at least 2 times per month up to weekly depending on the number of registered students, for both the fall and spring semester. Each student will be required to lead a discussion (2 hours) on an article which has been recently published, on a broad range of topics with focus on tissue repair after injury. All students will be encouraged to ask questions and participate in discussions. Student presentations will be augmented by the addition of interested postdoctoral fellows and faculty.

1 credit

This course will provide a general overview of fundamental developmental mechanisms and central concepts of development. This 5-week course covers early and intermediate developmental events, mouse molecular genetics and gene regulation. Comparative systems discussed include drosophila, zebrafish, Xenopus, chick, and mouse models. This course is intended for graduate students training in any aspect of biomedical research.

3 credits

Weekly ongoing Seminar Series Offered by the Dept of Regenerative Medicine and the Center for Digestive Diseases. This will be pass/fail as determined by attendance. This Seminar could satisfy the MCBP requirement for a seminar Series, both fall and spring semesters

1 credit

This journal club will meet weekly, for 1 hr per week, during both the Fall and Spring semesters. Each student will present and lead the discussion of a recent paper in the field of heart regeneration. All students will be expected to engage in discussion of the material. The attendance group will be augmented bt postdoctoral fellows and faculty who are interested in this topic.

1 credit

This is a journal club counting for 1 credit hour which will meet at least 2x per month up to weekly, depending on the number of registered students, for both the fall and spring semester. Each student will be required to lead a discussion (1-2 hours) on an article which has been recently published, on a broad range of topics with focus on digestive diseases. All students will be encouraged to ask questions and participate in discussions. Student presentations will be augmented by the addition of Interested postdoctoral fellows and faculty.

1 credit

Current and emerging topics in vision and ocular diseases will be presented and discussed in a journal club-style format. Students will present topics related to vision and ocular diseases using faculty-approved articles from peer-reviewed journals, and will be expected to actively participate in the discussion with other students, post doctoral fellows, and faculty members. Some presentation will be made by visiting and MUSC faculty members.

1 credit

The Cardiovascular Biology Journal Club course is designed to highlight the advances in cardiovascular science and medicine that will soon form the foundation for novel diagnostic, prognostic and therapeutic approaches to treating heart disease. Publications will be presented by the students weekly, which address current concepts of the cell and molecular biology bases of cardiovascular function, dysfunction and responsiveness to therapeutic interventions. Students, postdoctoral fellows and faculty who will take part in the weekly discussion include investigators from adult cardiology, adult Endocrinology, Cell Biology and Anatomy, Pharmacology, and Surgery.

1 credit

This multidisciplinary course addresses biochemical, applied, and translational approaches to the study of lipids. The course is composed of three main sections: lipid biosynthesis, lipid signaling, and lipids and disease. The first section is a comprehensive treatment of nomenclature and synthesis of major lipid classes including glycerophospholipids, sphingolipids, and sterols. The second section addresses roles of bioactive members of these lipid classes in regulation of cell signaling and downstream events. The third section is largely translational, with lectures on human diseases that involve the lipids and signaling pathways discussed. During the course, students will also gain an understanding for various techniques and current methodology used for lipid analysis. This course is open to graduate students, residents, fellows, postdocs, and third and fourth medical students.

1 credit

Research.

1 credit

Thesis.

1 credit

Master of Science in Clinical Research

An introduction to basic and intermediate statistical techniques used to analyze and interpret data in health sciences and related fields. Emphasis is on applications of these methods with just enough derivation to understand the procedures. Topics include descriptive statistics, graphical methods and probability with applications to epidemiology, discrete and continuous distributions, inference on means, nonparametric methods, and inference on proportions, contingency tables, correlation, analysis of variance, linear regression, logistic regression, and survival analysis. Students will not be expected to run computer programs, but will learn how to read printout in order to interpret analytical results.

3 credits

An emphasis will be placed on the concepts, study designs and procedures used in the implementation of clinical trials research studies. The methodology and process used to access and analyze data as well as the collection of data will be described.

3 credits

The objective of the course is to prepare the student to develop a draft grant application, the sections of a grant, IRB regulations and procedures, what reviewers look for and how to think like a reviewer, ethics, and developing a research budget. Students will be given examples of successful grants and grants that have not been funded to discuss and critique.

2 credits

This course is required for the Master of Science in Clinical Research. It is assumed that students in this class have a solid foundation in research design and both parametric and nonparametric statistics. An emphasis will e placed on the competencies and processes necessary to review the scientific literature. In particular, the students will review the published and unpublished literature associated with clinical research results. The focus of the class will be the review of the types of scientific and clinical research manuscripts, papers, and reports produced from different study approaches. The course will identify resources for the critical review of the scientific literature. The considerations and criteria for critical review of the literature will be addressed in the course. Students will prepare written critiques of selected literature and manuscripts. Prerequisites: MCR 700, 736, or permission.

2 credits

This course explores the scope of outcomes studies for evaluating the effectiveness of medical care by emphasizing the development of study designs matched to the research question. The course explores frequently used observation study designs, techniques for evaluating and selecting health outcomes measures, and analytical approaches appropriate to conducting health outcomes research. This course will also cover the approaches used for interpretation and translation of CER data through decision models to compare the cost effectiveness of treatments.

3 credits

This course provides an introduction to the discipline of epidemiology and its application to public health research and practice. The course is designed to provide a conceptual foundation for epidemiologic research and application, especially study designs, quantitative concepts and methods, analysis, and interpretation.

3 credits

This course is intended to introduce clinical researchers to research oriented data management and related basic topics in Informatics. Students taking this course will learn about basic concepts in: relational database design, modern research data capture tools, clinical data warehousing, security risks and mitigations, privacy issues in electronic data, data standards, data mining and other related topics. Students will get hands-on experience with using modern database tools to solve specific scientific problems by attending the course labs.

2 credits

An emphasis will be placed on the ethical issues associated with clinical research and practice. The focus of the class will be the review of the competencies involved in the conduct of ethically responsible research. The process of assessing ethical issues in research and study will be described. The ethical considerations in study design; study implementation, data management, data analysis, data Interpretation and results presentation and publication will be described.

1 credit

An emphasis will be placed on the competencies and processes associated with the concepts of team science in translational research necessary to review the scientific literature. Solving complex societal problems (e.g., environment, poverty, and cancer, health care) requires the integration of specialized knowledge bases.

1 credit

The seminar was created to meet the thematic area of the core competencies in cross disciplinary training for student awareness of current issues in medical education and to focus on contemporary research study designs and techniques for application in patient populations.

1 credit

Regression analysis is at the heart of statistics, and a sound knowledge of regression methods will serve students well as they design and conduct research projects. We begin with simple linear regression and then consider extensions such as multiple predictors, nonlinear effects, categorical predictors, and interactions. Students will learn to evaluate model fit using statistics such as t, F, and R2 in addition to informal analysis based on observable data patterns. We will rely heavily on graphical representations of the data and make use of plots of regression residuals. Concepts and techniques of regression analysis will be taught based on carefully developed examples. This course is intended for anyone involved in analyzing data, but who does not specialize in statistics. We will use computer software (mainly SAS) to examine data output, but students will only be required to read and understand the output. Students are not responsible for computer programming.

2 credits

This course provides a foundation for incorporating the principles and methods of CEnR, dissemination, and implementation in the development of community-academic research partnerships and implementing best practices. Topics include different levels of engaging communities in research, as well as the research processes, ethics and responsible conduct in CEnR, and various examples and strategies to involve communities in the research processes including dissemination and implementation of research findings.

1 credit

The course focuses on the conduct of clinical research, whether an investigator-initiated study or participation as a site in an industry sponsored clinical trial. It will present in detail all aspects of clinical research including assessment of opportunities and feasibility, building a budget, negotiating contracts, managing regulatory requirements, and understanding intellectual property.

2 credits

This course introduces the major historical and contemporary theories of leadership in the workplace with emphasis on the principles and application of leadership skills. The course will include the study of organization structures, principles, techniques and processes as they relate to practice in and management of health services and research organizations. Students should gain a better understanding of organizational behavior issues such as motivation, leadership, communication, interpersonal conflict, group dynamics, organizational structures, and meeting procedures.

2 credits

This course prepares participants to coordinate cost-effective health care research which protects the rights and safety of human subjects, achieves recruitment and retention outcomes and contributes to the science of health care. Participants completing the training will be prepared to coordinate research studies in compliance with the Good Clinical Practice Guidelines and federal regulations concerning human subject research. All participants of this course are required to take the CITI MIAMI Good Clinical Practice as a co-requisite.

1 credit

Innovation has become an important component of academic and scholarly activities and, as such, achievements in innovation should be recognized as essential part of the academic clinical research role, as well as a consideration for promotion and tenure for the academician in clinical research. Metrics and recognition in research, education, clinical services all covered in the course.

1 credit

The course will function as an interactive description of the research grand mechanisms, application process, review process and implementation. The different types of grants will be presented and the details of the application and peer review. The source of funding will be described. Students will learn an overview of the types of grants, potential funding sources, how to get started and resources available at MUSC.

1 credit

This course is taken online and prepares participants to coordinate cost-effective health care research which protects the rights and safety of human subjects, achieves recruitment and retention outcomes and contributes to the science of health care. Participants completing the training will be prepared to coordinate research studies in compliance with the Good Clinical Practice Guidelines and federal regulations concerning human subject research. All participants of this course are required to take the CITI MIAMI Good Clinical Practice and ICH Basic Course as a pre-requisite.

2 credits

This is a varied credit hour research course determined by the student and mentor. A contract between the two includes material covered and deliverables at the end of the semester.

1 credit

Neuroscience

NSCS 730A (this course), NSCS 730B and NSCS 730C will replace original Fundamentals of NS course, NSCS 730. This 2-credit course represents the first part of the introductory graduate sequence designed to provide an overview of the fundamental concepts in the field of neuroscience. The course covers the electrical properties of neurons, synaptic transmission along with an anatomical overview of mammalian (rat and human) brain. The class is primarily taught in lecture format but also includes an optional human brain dissection laboratory. NSCS 730A is prerequisite for the other two sister courses i.e., NSCS 730B and NSCS 730C that are also offered during the Spring semester. All 3 courses are required for students intending to join the neuroscience graduate program while NSCS 730A may be taken by non-neuroscience track students who are considering training in the neuroscience program.

2 credits

NSCS 730A, NSCS 730B(this course), and NSCS 730C will replace original Fundamentals of NS course, NSCS 730. This 2-credit course represents the second part of the introductory graduate sequence designed to provide an overview of the fundamental concepts in the field of neuroscience. The course covers an overview of the motor and sensory systems, associated circuits and anatomical structures. The class is primarily taught in lecture format but also includes an optional human brain dissection laboratory. NSCS 730A is prerequisite for NSCS 730B (this course) and NSCS 730C, and all 3 are required for students intending to join the neuroscience graduate track..

2 credits

NSCS 730A, NSCS 730B, and NSCS 730C(this course), will replace original Fundamentals of NS course, NSCS 730. This 2-credit course represents the second part of the introductory graduate sequence designed to provide an overview of the fundamental concepts in the field of neuroscience. The course covers an overview of the biochemical basis of neuropharmacology including neurotransmitters, their receptors and signaling. The class is primarily taught in lecture format. NSCS 730A is prerequisite for NSCS 730C (this course) and NSCS 730B, and all 3 are required for students intending to join the neuroscience graduate track..

2 credits

This course is the second component of the introductory graduate sequence designed to provide an overview of the fundamental concepts in the field of neuroscience. Building upon the anatomy and physiology covered in Fundamentals of Neuroscience, this course covers the development and plasticity of the nervous system, higher brain functions such as memory and language, and clinical neuroscience. The class is taught in lecture format. A background in basic biology or permission of the instructor is required.

5 credits

This elective adds to the Neuroscience Core by introducing students to the computational models that can be used to describe the dynamics of neural networks. The lecture-based merit credit course introduces the theoretical study of differential equations, Markovian models, and then applies such models to simulations of neural networks. Student learning is assessed through problem sets and exams.

2 credits

A laboratory offered to graduate students in neuroscience and bioimaging to study human neuroanatomy.

1 credit

This lecture-based merit credit course features a variety of introductory topics on the mathematics of vectors and matrices, which are necessary components for the analysis of neural data. The course culminates by introducing clustering methods that are commonly used in neuroscience. Student learning is assessed through problem sets and exams.

2 credits

This course adds to the Neuroscience Core by introducing students to the use of programming methods of perform data analysis on neural recordings. The project-based merit credit course provides a focused introduction to coding techniques, and encourages students to learn both fundamental principles and practical strategies underlying the design of computational code. Exercises will be based in a stable, contemporary coding language (Matlab), though the choice of language may be updated as the field moves forward. Student learning is assessed through the completion of a final project.

1 credit

This elective adds to the Neuroscience Core by introducing students to complex statistical analyses that allow them to test properties in systems that are poorly described by normal parametric models. The lecture-based merit credit course features a variety of advanced topics in non-parametric statistical methods, including Monte Carlo sampling etc. The goal of the course is for students to learn principles underlying statistical testing, as opposed to specific statistical tests. Student learning is assessed through problem sets and exams.

2 credits