AI/ML Seminar Series: Effective representation learning to dissect the gene regulatory grammar

Jing Zhang
Assistant Professor, Department of Computer Science
University of California, Irvine

The recent advances in sequencing technologies provide unprecedented opportunities to decipher the multi-scale gene regulatory grammars at diverse cellular states. Here, we will 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 a deep generative model, named SAILER, to learn the low-dimensional latent cell representations from single-cell epigenetic data for accurate cell state characterization. SAILER adopted the conventional encoder-decoder framework and imposed additional constraints for biologically robust cell embeddings invariant to confounding factors. Then at the network level, we developed TopicNet using latent Dirichlet allocation (LDA) to extract latent gene communities and quantify regulatory network connectivity changes (network “rewiring”) between diverse cell states. We applied our TopicNet model on 13 different cancer types and highlighted gene communities that impact patient prognosis in multiple cancer types.

Bio: Dr. Zhang is an Assistant Professor at UCI. Her research interests are in the areas of bioinformatics and computational biology. She graduated from USC Electrical Engineering under the supervision of Dr. Liang Chen and Dr. C.C Jay Kuo. She completed her postdoc training at Yale University in Dr. Mark Gerstein’s lab. During her postdoc, she has developed several computational methods to integrate novel high-throughput sequencing assays to decipher the gene regulation “grammar”. Her current research focuses on developing computational methods to predict the impact of genomic variations on genome function and phenotype at a single-cell resolution.

YouTube Stream: https://youtu.be/HPPq5Xvlr9c

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