CSE 527 Computational Biology

General Information

Time: Tuesdays and Thursdays, 10-11:20 am

Location: Bill & Melinda Gates Center (CSE2) G01


Instructor: Su-In Lee

Teaching Assistants: Ethan Weinberger & Joseph Janizek


Overview

CSE 527 introduces artificial intelligence (AI) and machine learning (ML) methods for understanding biological systems and improving health care. Research in computational biology used to be driven by the availability of new types of large-scale biological datasets. Now, AI/ML is transforming biology and medicine and shapes the biological or biomedical knowledge we extract from these data. The focus of this course is to understand how ML advances enable new questions and solutions so we learn how to advance the field of computational biology and to do innovative research in the field.

AI/ML techniques such as explainable AI, deep learning, and probabilistic inference are covered. Problem areas involve biological problems at multiple scales - genome, epigenome, transcriptome (gene expression), proteome, and phenome - and health, disease, and therapy development. This year, we will feature some of the recent research done in Prof. Lee's Allen School AIMS lab.


  • Introduction (5 lectures)

    • Basic knowledge in ML and computational biology required for the course

  • Genetics & genomics (3 lectures)

  • Transcriptomics – gene expression data analysis (4 lectures)

  • Single-cell genomics (2 lectures)

  • Biomarker discovery for precision medicine (1 lecture)

  • Proteomics (e.g., AlphaFold) (2 lectures)

  • Medicine & healthcare (2 lectures)


Grading

  • 50%: Project

    • 5%: Proposal

    • 10%: Checkpoint writeup

    • 5%: Draft

    • 15%: Final presentation

    • 15%: Final report

  • 10%: Project Workshopping (Review and provide feedback on another team's materials)

  • 30%: Paper Discussions

    • 20%: Reading-related discussion board posts

    • 10%: Discussion leading

  • 10% Participation