Past talk · AI/ML Seminar Series
Effective representation learning to dissect the gene regulatory grammar
Assistant Professor, Department of Computer Science, University of California, Irvine
- Date & time
- Monday, May 24, 2021 · 1:00 PM
- Location
- Online (live stream)
Abstract
Recent advances in sequencing technologies provide unprecedented opportunities to decipher multi-scale gene regulatory grammars at diverse cellular states. I introduce our computational efforts on cell/gene representation learning to extract biologically meaningful information from high-dimensional, sparse, and noisy genomic data. First, we proposed SAILER, a deep generative model to learn low-dimensional latent cell representations from single-cell epigenetic data that are invariant to confounding factors. Then at the network level, we developed TopicNet using latent Dirichlet allocation to extract latent gene communities and quantify regulatory network rewiring between cell states, applied across 13 cancer types.
About the speaker
Dr. Jing Zhang is an Assistant Professor at UC Irvine. Her research interests are in bioinformatics and computational biology. She completed her postdoc training at Yale in Mark Gerstein's lab, developing computational methods to integrate high-throughput sequencing assays to decipher gene regulation. Her current research focuses on predicting the impact of genomic variation on genome function and phenotype at single-cell resolution.