CS&E Colloquium: Network-based Machine Learning Methods for Spatial Genomics: A Generalization to High-order Data and Multi-relational Graphs
The computer science colloquium takes place on Mondays from 11:15 a.m. - 12:15 p.m.
This week's speaker, Rui Kuang (University of Minnesota; a member of the data science faculty), will be giving a talk titled "Network-based Machine Learning Methods for Spatial Genomics: A Generalization to High-order Data and Multi-relational Graphs".
Abstract
Biological tissues are composed of different types of structurally organized cell units playing distinct and cooperative functional roles to drive phenotypes such as diseases. The recent spatial transcriptomics technologies have enabled spatially-resolved RNA profiling of single cells mapped with cell identities and localizations for understanding the cells’ organizations and functions. In this talk, I will present a family of network-based machine learning methods that my lab developed to decode the signals in the genomic data for predicting phenotypes. The network-based methods introduce prior information from biological networks and other knowledge graphs for learning with highly structured genomic data. I will explain the biological intuitions, mathematical formulations and algorithms, and experimental results of the network-based learning methods with a focus on how the network-based modeling can be generalized to high-order tensor structures in the new spatial transcriptomics data guided by a multi-relational graph to encode cell spatial information and gene functional information as prior information. I will also provide our perspective on the importance of modeling high-order structures for analyzing spatially-resolved transcriptomes and biological networks and discuss our future plan on developing such high-order learning methods for broader applications in bioinformatics.
Biography
Dr. Rui Kuang is an associate professor in computer science and engineering at the University of Minnesota Twin Cities. His lab is interested in developing machine learning models and algorithms for phenome-genome association analysis by mining knowledge graphs, and phenotype prediction and biomarker identification from gene expression profiling data using network-guided machine learning methods. His lab developed high-order relational learning and meta-analysis methods for integrative studies of multiple knowledge graphs, and single-cell and spatially resolved transcriptomic data. Dr. Kuang is a recipient of NSF CAREER Award in 2011. He received his PhD from Columbia University in 2006, MS from Temple University in 2002 and BS from Nankai University in 1999, all in computer science.