QLS/Ludmer Lectures – Dr Sara Mostafavi

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

QLS Seminar Lecture, 10th floor, room 1034: 12:00-1:00 pm

Dr S Mostafavi: Combining genomics data to predict function of the non-coding genome.

Abstract: The recent availability of diverse genome-wide assays, including ATAC-Seq, ChIP-Seq, and RNA-Seq, now enables researchers to quantify, at a high resolution, the cellular and context-specific activity of every segment of DNA. Combining genetic data with these other genomics assays provide an opportunity to a) decode DNA, for example by inferring the sequence code underlying functional differences between cell types within an individual, and b) predict the impact of variation in a given base of DNA on cellular function. However, interpreting this data to extract biological insights requires disentangling meaningful, and hence reproducible and consequential associations, from mere correlations (i.e. spurious associations). In this talk, Dr Mostafavi will present statistical and machine learning approaches for integrating heterogeneous data, in order to find robust associations. First, focusing on the task of finding associations between genetic variation and cellular (expression) traits in a population-based study, she will review methods for inferring and accounting for hidden confounding factors and then will describe new approaches based on latent variable modeling to infer context-specific associations. Second, She will describe efforts in using deep learning approaches for combining genetic and ATAC-Seq data across a large set of immune cells to learn non-coding motifs across the genome.  

Lunch for all participants, 6th Floor Foyer: 1:00-1:30 pm

 

Ludmer Methods Presentations, 5th floor, Jonathan C. Meakins Amphitheatre (room 521)1:30-3:30 pm

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).

Contact Information

Joanne Clark, Ludmer Centre Neuroinformatics & Mental Health   | Administrative Director |  joanne.clark@mcgill.ca 

Alex DeGuise, QLS Coordinator | Quantitative Life Sciences |  communications.qls@mcgill