2025 Fall CS582 Machine Learning for Bioinformatics

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Course description

In this graduate-level course, we will introduce foundational and state-of-the-art machine-learning approaches to key problems in life sciences and biology, with an emphasis on deep learning for molecular engineering. The course is structured into four main modules: functional genomics, protein language models, geometric DL, and protein structure models. We will cover a wide range of DL architectures (CNN, Transformer, GNN, etc.), generative models (e.g., language models and diffusion) and machine learning techniques (supervised, unsupervised, optimization), and discuss their applications to modeling, understanding, and designing the sequence, structure, and functions of genomes and proteins. Students will learn about both theoretical concepts and applications to biological problems, and gain practical experience through implementing solutions to problem sets, reading papers, and conducting final course project.

Logistics

• Lecture: Wed/Fri 11:00 - 12:15 in 1302 Siebel Center for Comp Sci
• Office hours: Wednesday 1pm-2pm in 3212 Siebel Center for Comp Sci
• TA office hours: Friday 4-5pm in Siebel Center floor -1

Grading

Grading will be based upon four problem sets containing either programming questions or written questions (40%), a Course Project (35%), reading assignments (20%), and participation (5%). Attendance in lecture is important as the class moves quickly and you will need to be present. You can use three late days for problem set deadlines (or email the course staff).

Syllabus and schedule

Date	Lecture	Title	Assignment
8/27/2025	Lecture 1	Logistics and intro
8/29/2025	Lecture 2	ML foundations
9/3/2025	Lecture 3	ML foundations
Module I: Functional Genomics
9/5/2025	Lecture 4	DL for functional genomics I
9/10/2025	Lecture 5	CNN/ResNet
9/12/2025	Lecture 6	RNN, LSTM, StateSpaceModel(S4,Mamba,Hyena)	Reading assignment 1
9/17/2025	Lecture 7	Model uncertainty and interpretability	Problem set 1 out
9/19/2025	Lecture 8	DL for functional genomics II
Module II: Protein Language Models
9/24/2025	Lecture 9	Protein sequenece modeling I
9/26/2025	Lecture 10	Transformer and MLM I
10/1/2025	Lecture 11	Transformer and MLM II	Reading assignment 2
10/3/2025	Lecture 12	Protein sequenece modeling II	Problem set 1 due, Finalize project team-up
10/8/2025	Lecture 13	Protein function prediction (sequence based)	Problem set 2 out
10/10/2025	Lecture 14	Protein function optimization (sequence based) I
10/15/2025	Lecture 15	Protein function optimization (sequence based) II
Module III: Intro to Geometric DL
10/17/2025	Lecture 16	Graph Neural Networks(GNN) I
10/22/2025	Lecture 17	Graph Neural Networks(GNN) II and Intro to Geometric DL	Problem set 2 due, Reading assignment 3
10/24/2025	Lecture 18	Geometric DL II & Inverse Folding	Problem set 3 out
10/29/2025	Lecture 19	Geometric DL III
Module IV: Protein Structure Models
10/31/2025	Lecture 20	Protein structure prediction I	Project proposal due
11/5/2025	Lecture 21	Protein structure prediction II	Reading assignment 4
11/7/2025	Lecture 22	Protein interaction, function prediction
11/12/2025	Lecture 23	Generative models primers: VAE,Diffusion, Flow Matching I	Problem set 3 due
11/14/2025	Lecture 24	Generative models primers: VAE,Diffusion, Flow Matching II
11/19/2025	Lecture 25	Generative model for protein design I	Problem set 4, Reading assignment 5
11/21/2025	Lecture 26	Generative model for protein design II
Final projects
11/26/2025	No lecture	Fall break
11/28/2025	No lecture	Fall break
12/3/2025	Lecture 27	No lecture
12/5/2025	Lecture 28	No lecture
12/10/2025	Lecture 29	Project Presentations

Prerequisites

Linear Algebra, python programming, intro to ML.

Project

This subject has a substantial project component. We recommend working on projects in a team of 2-3 students. You are free to choose any problem related to the lectures of the course, and develop a deep learning solution.