Génome Québec is proud to announce its financial participation in nine genomics projects selected for funding under the IVADO Fundamental Research Funding Program.
Genomics technology will generate massive amounts of data, which will be of significance for the human health sector. Our ability to analyze and interpret these data is a critical factor for the successful integration of genomics into the Québec health care system. The convergence of genomics and AI provides us with the possibility of making optimal use of clinical data from genomics: the research projects selected will lead to the development of tools used to process, analyze and integrate huge volumes of complex data generated by omics technologies and lead to advances in machine learning models that can predict the development, progression and response to therapy in diseases, such as epilepsy, cancer and heart disease.
Congratulations to the researchers:
– Patrick Cossette (Université de Montréal)
Towards personalized medicine in the management of epilepsy: a machine learning approach in the interpretation of large-scale genomic data
– Benoit Coulombe (Université de Montréal) – Institut de recherches cliniques de Montréal
A machine learning approach to decipher protein-protein interactions in human plasma
– Julie Hussin (Université de Montréal) – Institut de Cardiologie de Montréal
Deep Learning Methods in Biomedical Research: from Genomics to Multi-Omics Approaches
– Sébastien Jacquemont (Université de Montréal) – Centre de recherche du CHU Sainte-Justine
Modeling and predicting the effect of genetic variants on brain structure and function
– Frederic Leblond (Polytechnique Montréal)
Machine learning technology applied to the discovery of new vibrational spectroscopy biomarkers for the prognostication of intermediate-risk prostate cancer patients
– Éric Lécuyer (Université de Montréal) – Institut de recherches cliniques de Montréal
Developing a machine learning framework to dissect gene expression control in subcellular space
– Sébastien Lemieux (Université de Montréal) – Institute for Research in Immunology and Cancer
Deep learning for precision medicine by joint analysis of gene expression profiles measured through RNA-Seq and microarrays
– Pierre Thibault (Université de Montréal) – Institute for Research in Immunology and Cancer
Matching MHC I-associated peptide spectra to sequencing reads using deep neural networks
– Jean-Claude Tardif (Université de Montréal) – Montreal Heart Institute
Machine learning and precision medicine to curb atherosclerosis – Please note that this last team is funded exclusively by Génome Québec.
To read the press release, click here.