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      QLS/Ludmer Lectures – Dr Sara Mostafavi in Montreal


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      September 17, 2019

      Tuesday  12:00 PM

      McGill University , 3655 Promenade Sir-William-Osler
      Montreal, Quebec

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      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  Location: McIntyre Medical Sciences Building, McGill University, 3655 Promenade Sir-William-Osler, Montreal, QC H3G 1Y6 (Rooms vary!)  Program 12:00-1:00 pm: QLS Seminar Lecture, 10th floor, room 1034: Dr S Mostafavi: Combining genomics data to predict function of the non-coding genome. 1:00-1:30 pm: Lunch for all participants, 6th Floor Foyer 1:30-3:30 pm: Ludmer Methods Presentations, 5th floor, Jonathan C. Meakins Amphitheatre (room 521): S Mostafavi: Prediction of gene function from molecular networks, using graph-based label propagation algorithms for data integration and predictions. Ludmer trainees and junior investigators will present their method research 1:30-3:30 pm: Reception, 6th floor Foyer. 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. 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.   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. Contact: Joanne Clark, Ludmer Centre Neuroinformatics & Mental Health   | Administrative Director |  joanne.clark@mcgill.ca 

      Categories: Education | Science

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