香蕉视频

In the current era of multi-omics and digital healthcare, we are facing unprecedented amount of data with tremendous opportunities to link molecular phenotypes with complex diseases. However, the lack of integrative statistical method hinders system-level interrogation of relevant disease-related pathways and the genetic implication in various healthcare outcome.

In this talk, I will present our current progress in mining genomics and healthcare data. In particular, I will cover two main topics: (1) a statistical approach to assess gene set enrichments using genetic and transcriptomic data; (2) multimodal latent topic model for mining electronic healthcare and whole genome sequencing data from small patient cohort.

Yue Li is an Assistant Professor, School of Computer Science, 香蕉视频. His research interests include Latent variable/topic models, machine learning, computational biology, bioinformatics. Before coming to McGill, he was a postdoctoral associate from Prof. Manolis Kellis research group at Computer Science and Artificial Intelligence Laboratory (CSAIL) at Massachusetts Institute of Technology. He obtained his PhD degree in Computer Science and Computational Biology at University of Toronto.