Trustworthy machine learning for understanding genome regulation
Maxwell Libbrecht - Simon Fraser University
March 6, 2026, 2:30 p.m. - March 6, 2026, 3:30 p.m.
MC 321
Hosted by: Mathieu Blanchedtte
Genome-wide association studies (GWAS) have identified tens of thousands of genetic variants associated with human diseases and traits, but the vast majority of these associations are not backed by a hypothesized mechanism. Understanding such disease-associated variants is hampered by the incompleteness of annotations functional elements in the genome. To improve our annotation of genomic elements, large-scale projects like ENCODE, CEEHRC and IHEC have recently engaged in epigenome mapping. These projects are enabled by high-throughput sequencing techniques for genome-scale measurement of biochemical activity of chromatin in cellular samples. These datasets quantify various facets of gene regulation such as genome-wide measurements of transcription factor binding or histone modifications using ChIP-seq, measurements of open chromatin using DNase-seq or ATAC-seq, RNA transcription using RNA-seq, and others. My group aims to improve our understanding and annotating of the genome using machine learning. I will present our recent work aiming to build and statistically validate genomic models for understanding and annotating genome regulation.
Maxwell Libbrecht is an Assistant Professor at the School of Computing Science at Simon Fraser University. His research focuses on developing machine learning methods applied to high-throughput genomics data sets. He received his PhD in 2016 from the Computer Science and Engineering department at University of Washington, advised by Bill Noble and Jeff Bilmes, and his undergraduate degree in Computer Science from Stanford University, advised by Serafim Batzoglou. He is a 2021 Michael Smith Foundation Scholar. He was the first author of a paper named one of ISCB’s Top 10 Regulatory and Systems Genomics papers of 2015.
