Quantitative Life Sciences (QLS) and Ludmer Centre – Joint interdisciplinary lectures
Guest speaker: Dr Sara Mostafavi
Applying machine learning and statistical methods to study the genomics of complex diseases, psychiatric disorders and cancers.
Tuesday, Sept.17, 2019
McIntyre Medical Sciences Building, McGill University, 3655 Promenade Sir-William-Osler, Montreal, QC H3G 1Y6
1. S. Mostafavi: Prediction of gene function from molecular networks, using graph-based label propagation algorithms for data integration and predictions.
2. Ludmer trainees and junior investigators will present their method research.
Reception, 6th floor Foyer: 3:30-4:30 pm.
Register via Eventbrite for free.
Open to faculty and students from McGill University, Concordia University, University of Montreal and UQAM
Dr Sara Mostafavi
Assistant Professor, Departments of Statistics and Medical Genetics, University of British Columbia (UBC), faculty member, Vector Institute, Canada Research Chair (CRC II) in Computational Biology, Canada CIFAR Chair in Artificial Intelligence (CIFAR-AI), and a CIFAR fellow in the Child and Brain Development program.
The production of diverse types of high-dimensional and high-throughput biological data has increased tremendously in the last decade, presenting novel opportunities to develop and apply computational and machine learning approaches to understand the genetics of human diseases. However, the high dimensionality of this data, whereby up to millions of diverse and heterogeneous “features” are measured in a single experiment, coupled with the prevalence of systematic confounding factors present significant challenges in disentangling bona fide associations that are informative of causal molecular events in disease.
Dr Mostafavi’s lab focuses on designing tailored computational models and algorithms for integrating multiple types of high-dimensional “omics” data, with the ultimate goal of disentangling meaningful molecular correlations for common diseases such as psychiatric disorders and cancers.
This event is part of the jointly organized weekly QLS lecture series aimed at training interdisciplinary researchers capable of harnessing the potential offered by big-data initiatives and neuroinformatics technologies. Partners: the Quantitative Life Sciences (QLS) Phd program, Ludmer Center Neuroinformatics & Mental Health, the Centre for Applied Mathematics in Bioscience and Medicine (CAMBAM), and the McGill initiative in Computational Medicine (MiCM).