Teaching

Advanced topics in Machine Learning for Computational Biology

Elective Course (CS/Bioinformatics M.Sc. open to undergraduates), Tel Aviv University, School of Computer Science, 2024

Summary

Data-driven science is a ubiquitous paradigm in modern biology: heaps of data are collected on a biological system of interest, from which one must discover qualitative scientific insights or build accurate quantitative models. This course is an overview of advanced machine learning algorithms commonly used in modern computational biology research. The goals are three-fold: i) Learn the underlying mathematical principles behind these algorithms ii) Learn how and when to use them for scientific purposes iii) Understand their limitations. The algorithms will be illustrated on various biological systems (brain recordings, single cell data, protein sequences, molecules, etc.). No prior knowledge of biology is required. Course will be given in English. Evaluation will be based on oral presentation of research articles and home assignments. Tentative syllabus below, is subject to changes.

Workshop on AI Algorithms for Structural Biology

Workshop (Bioinformatics/CS Undergraduates), Tel Aviv University, School of Computer Science, 2023

Summary

Proteins form the molecular basis of all life, and yet, we lack reliable means to predict what they do, how they do it, where and when. Indeed, although their primary constituents - amino acids - are well-characterized, the size and diversity of proteins make physical simulations of their behavior extremely challenging. To overcome these challenges, Machine learning approaches have become increasingly popular since the 90’s for predicting how proteins fold, recognize one another and function. While initially only moderately successful, deep learning has enabled radical progress across tasks, culminating with the celebrated AlphaFold algorithm for protein structure prediction in 2021. This workshop is a joint introduction to machine learning and structural biology, with an emphasis on practical tools and current research topics. Prior knowledge of either domain is desirable but not necessary. In the first part of the semester, we will cover key concepts including protein structures & folding, supervised learning and deep learning. Then, we will discuss the biological and algorithm concepts underlying AlphaFold. The second part of the semester will be dedicated to group research projects related to the topic. Evaluation will be based on the project reports and presentations. The course will be in English.