QLS Talk: Biomarker discovery when applying machine learning techniques to large RNA-Seq Cohorts
Channel: Ludmer Centre
Date: 2020-10-20

McGill’s Quantitative Life Sciences and Medicine lecture series 2020/21

Title: Challenges and opportunities for biomarker discovery when applying machine learning techniques to large RNA-Seq Cohorts

Guest: Sebastien Lemieux, University of Montreal

Date: Sept. 29, 2020

Abstract: Over two decades of statistical developments have allowed transcriptomics, from microarrays to RNA-Seq, to become an indispensable tool to characterize changes in expression profiles. Computationally, a central step is the application of specialized parametric statistical tests such as DESeq2, EdgeR or Voom to single out differentially expressed genes. In parallel, several new tricks have been developed within the machine learning framework to facilitate the training of high-dimensional classifiers that can take advantage of the whole transcriptome characterizations. Unfortunately, identifying biomarkers from these trained classifiers has proven more difficult than expected. These computational advances, coupled with reduced costs and protocol stabilization for RNA-Seq has led to the emergence of large cohorts of hundreds of high-quality RNA-Seq expression profiles. I will show, using large datasets developed to refine sub-typing of acute myeloid leukemia (AML), that standard statistical tools reveal their inadequacy for the identification of biomarkers. I will present a novel approach to the identification of biomarkers based on machine learning principles that scales well with large datasets.

About the lecture series:
McGill’s Quantitative Life Sciences (QLS) PHD program, the Centre for Applied Mathematics in Bioscience and Medicine (CAMBAM), the McGill initiative in Computational Medicine (MiCM), and the Ludmer Center for Neuroinformatics & Mental Health have join forces to offer this weekly interdisciplinary seminars. To see the program and abstracts, as well as information on the PhD program, visit: https://www.mcgill.ca/qls/seminars .