This course is designed to equip students with core concepts and fundamental skill sets needed for biomedical research. It combines traditional didactic lectures with small group and individual learning activities, problem solving exercises, workshops, and hands-on analyses of data sets. The course emphasizes the integration of topics spanning the fields of biochemistry, cell and molecular biology, and genetics.

Students will learn the basic concepts, technology, and processes that guide the practical use of common statistical methods. The course introduces descriptive and inferential statistics and applications to real-world data. Students will reinforce learning with problem sets, a publicly sharable R portfolio, and a final project to achieve practical competence in the use of statistical software and interpretation of results.

The course will expand students' statistical programming skills to utilize disparate datasets to generate conclusions about complex questions. Students will reinforce learning with problem sets and assignments to achieve competence in the use of statistical software to clean and organize data and apply the correct statistical approach (ANOVA, Chi-Square, regression, multiple regression) to interpret results.

Students will learn theoretical and practical foundations of computational methods for analysis of experimental data from various biological data types. Course will cover algorithms, statistical and computational methods, and application areas in computational biology, and will include both classical methods as well as recent advances. Prior coursework/experience in linear algebra, UNIX, and R & Python programming required.

Students will continue study in more advanced areas of computational biology, covering more advanced models, algorithms, and computational methods as applied to a variety of biological data types. Students will study theory and practice of machine learning methods commonly used in biology and implement and apply these models in various areas of biology.

This course introduces students to biomedical research. Students conduct one or more research projects of limited scope under the direction of faculty and lab members. It is open only to graduate students in Biomedical Sciences (BIMS).

The Research in Progress Colloquium is a series of research seminars and short talks by students in our combined M.D./Ph.D. Program. The major goals of the course are to familiarize students with key research areas of importance for training as physician scientists, and to develop the student's presentation skills. Students are required to give a minimum of one oral presentation per year to their fellow students and to selected faculty members who have expertise in the area of presentation. Students also are required to attend presentations of other students and to participate in group discussions. In addition to research presentations by students, there will also be presentations by faculty members in areas of significance for training of physician scientists. Grading (S/U) will be based on the quality of the students' presentation, as well as the extent of their participation in group discussions.

Beginning in 1989, the National Institutes of Health introduced a requirement that institutions provide a program of instruction in the responsible conduct of research (NIH Guide for Grants and Contracts, Volume 18, Number 45, 1989). This was later expanded to require that all fellows on NIH training grants should receive instruction in the responsible conduct of research. The requirement does not specify a particular format or curriculum. However, recommendations are made that several areas should be covered in the instruction: conflict of interest, responsible authorship, policies for handling misconduct, policies regarding the use of human and animal subjects, and data management. This course is designed to help student consider each of these areas and therein formulate an understanding of responsible conduct in research.

The course will cover human genetics and genomics, including the human/mammalian genome variation, determination of genomic variation on phenotype and disease risk, mapping and characterizing genetic variants on phenotype, determining the putative impact of genetic variants on gene expression (transcriptomics, epigenomics), the promise and implications of genome science on precision medicine and the ethical, legal & social implications.

This second course focuses on generating data with common biological techniques and understanding the foibles of observation, assay development and scrutiny, and the effects of instrumentation.