CSE 527 Computational Biology

General Information

Time: Tuesdays/Thursdays, 10:00 AM - 11:20 AM (first lecture: 9/26)

Location: Bill & Melinda Gates Center (CSE2) G04

Instructor: Su-In Lee

Teaching Assistant: Patrick Yu

Overview

CSE 527 introduces the principles of artificial intelligence (AI) and machine learning (ML) to dissect biological systems and enhance healthcare. The evolution of computational biology once hinged on the availability of extensive biological datasets. However, the landscape has shifted, with AI/ML driving transformative changes in biology and medicine. These technologies mold the extraction of biological and biomedical insights from data. This course centers on grasping how AI/ML breakthroughs inspire fresh inquiries and solutions, empowering us to propel computational biology forward and undertake groundbreaking research.

The curriculum encompasses a spectrum of AI/ML techniques, including explainable AI, deep learning, and probabilistic inference. It addresses biological quandaries spanning various scales, from the genome and epigenome to the transcriptome (gene expression), proteome, and phenome. Moreover, it delves into healthcare applications, encompassing aspects related to 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