Experiential Learning in Clinical Research (Minimum of 4 units required):
Translational Regenerative Medicine - 2 units - CLRE-237
This course covers the basics of regenerative medicine for understanding what is a stem cell targeted therapy and what are the principles for taking a cell-based product from pre-clinical to first-in-human clinical studies. Topics covered include the regulatory, ethical, and study design considerations for developing stem cell targeted therapies. Students will grasp the complexities of stem cell targeted therapies and be able to understand the framework for developing new products. This course provides a good overview for those entering a field that may deliver stem cell therapies, stem cell targeted treatments, or other cellular based therapies (e.g., car-t-cell).
Applied Translational Research - 2 units - CLRE-238
Students will gain a comprehensive and integrative operational understanding of an entire life science innovation cycle, from drug idea to market and back, through case studies done by mining publicly disclosed information in teams mentored by biopharmaceutical professionals. Prerequisites: Translational Research Fundamentals (CLRE 236) or consent of department.
Experiential Learning in Clinical Research - 2 units - CLRE-270
This course is designed to complement and apply certain fundamental principles in the CLRE Scientific Communication course by providing a deep-dive into processes, strategies, and activities associated with publishing in the peer-review literature. Specifically, the course will provide instruction on key topics including research topic formulation, journal content types, how to draft specific manuscript elements, targeting journals and the submission process, navigating the peer-review process, types of research tools and software, how to conduct academic presentations, and strategies for translation and dissemination. In total, students will be taken through the full journey of publishing in the peer-review and also taught skills on how to ensure their research is impactful.
Advanced Statistics Electives (Minimum of 4 units required):
Longitudinal Data Analysis - 2 units - CLRE-263
This class will introduce you to the statistical methods and techniques for analyzing medical data from longitudinal studies using PASW/SPSS software. You will gain an understanding of the challenges and statistical issues for designing and analyzing longitudinal studies, recognizing and using longitudinal data analysis methods, and performing analysis. Prerequisites: Biostatistics I & II (CLRE 253 & CLRE 254) or consent of department.
Advanced Regression Methods - 2 units - CLRE-265
This course will expose and familiarize you with important advanced statistical methods such as methods for numeric outcomes (Linear regression), non-linear regression, binary outcomes (Logistic regression), counts (Poisson regression), and categorical outcomes (Log-linear models.) Prerequisite: Biostatistics I (CLRE 253). Corequisite: Biostatistics II (CLRE 254) or consent of department.
Advanced Statistics Using R - 2 units - CLRE-267
This course introduces biostatistical methods used in more advanced clinical research work, including longitudinal data analysis, meta-analysis, predictive modeling (LASSO, random forests, neural networks), competing risks survival analysis. Uses the R statistical package. Prerequisite: Biostatistics I (CLRE 253). Corequisite: Biostatistics II (CLRE 254) or consent of department.
General Electives (Minimum of 2 units are required):
Current Trends in Biomedical Informatics - 1 units - MED-262
Weekly talks by researchers introduce students to current research topics within BMI. Speakers are drawn from academia, health care organizations, industry, and government. This is a required course for the Biomedical Informatics track, and an elective for the Bioinformatics and Systems Biology track.
Bioinformatics Applications to Human Disease - 4 units - MED-263
Students learn background knowledge and practical skills for investigating the biological basis for human disease. Using bioinformatics applications, they: (1) query biological and genetic sequence databases relevant to human health, (2) manipulate sequence data for alignment, recombination, selection, and phylogenetic analysis, (3) normalize microarray data and identify differentially expressed genes and biomarkers between patient groups, (4) annotate protein data and visualize protein structure, and (5) search the human genome and annotate genes relevant to human diseases.
Principles of Biomedical Informatics - 4 units - MED-264
Students will understand the main challenges of computing with phenotypes, how to integrate molecular data into electronic medical records and clinical trial records. They will get an introduction to medical decision making, consisting of introduction to decision theory, clinical decision support systems, clinical predictive models, as well as biomedical ontologies, standards, and data repositories. Students will know how to structure and query clinical data sets, and how the most commonly used privacy technologies can be used to avoid confidentiality breaches in de-identified disclosed datasets.
CER/Comparative Effectiveness Research - 2 units - CLRE-266
CER is the conduct and synthesis of research comparing the benefits and harms of different interventions and strategies to prevent, diagnose, treat and monitor health conditions. This course will provide you with an update on CER methods and a review of the critical literature in this emerging field.
Modeling Clinical Data/Knowledge for Computation - 2 units - MED-267
This course will describe existing methods for representing and communicating biomedical knowledge. The class will describe existing health care standards and modeling principles required for implementing data standards, including biomedical ontologies, standardized terminologies and knowledge resources.
Statistics Concepts for Biomedical Research - 4 units - MED-268
This course focuses on standard statistical methods and experimental design as well as predictive modeling, natural language processing and information retrieval. The course also provides in-depth coverage of evaluation methods and design of experiments for machine learning and statistical learning methods. Students perform statistical analyses using R statistics software and critique statistical results in published research.
Introduction to Biomedical Natural Language Processing - 4 units - MED-277
Biomedical Natural Language Processing (BioNLP) is an essential tool in both biomedical research and clinical applications. Students taking this course will learn how to process free text data and their integration with other types of biomedical data with BioNLP.
A Note on MED Courses
Non-CLRE courses (courses beginning with "MED") are not offered by our department; they are offered by School of Medicine (SOM). You must obtain approval to enroll in these courses PRIOR to the quarter's registration deadline. To obtain approval, you must email the instructor of the course directly for permission to enroll and include email@example.com and firstname.lastname@example.org in the email. You must also notify the MAS Graduate Program Coordinator (email@example.com) so it will count towards the appropriate MAS program requirement.