Basics of AI and Data Science in BioMedicine

Catalog description

This course focuses on cutting-edge computational methods in biomedical research. Moving beyond traditional biostats approaches, the curriculum emphasizes AI, machine learning, and state-of-the-art techniques in genomics and transcriptomics. Topics range from spatial transcriptomics and single-cell sequencing analysis to mathematical modeling of cancer treatments. The course is broken into three main sections. The first month will focus on 'fundamentals' of bioinformatics: during this section, students will be introduced to the range of bioinformatic approaches currently being used, from the techniques themselves, to analysis methods, to interpreting results. The second section will be a hands-on 'coding bootcamp' which will be four hands-on practical sessions using Google Colaboratory and markdown style coding notebooks. Students who are already proficient coding in either Python or R will have the option to complete a packet of take home assignments in lieu of the bootcamp. In the final section of the course, we will give students a tour of a series of special topics in applications of AI and machine learning to biomedicine. Topics will range from ethics of AI to radiomics and medical imagine, to mathematical modeling of tumor and pathogen growth and evolution. We hope that this structure will allow students to gain an appreciation of the kind of research questions that are pursued and the diversity of methods used to analyze large, complex, biological data. This course aims to prepare students for the evolving landscape of computational biology and its applications in molecular and biomedical research.