Research Projects

Use of gene expression profiles for prediction of biological activity of compounds

Léa Kaufmann

Using deep learning diffusion models for denoising of low-coverage RNA-seq data

Using deep learning diffusion models for denoising of low-coverage RNA-seq data

Identification of chemotherapy-induced transposable elements in triple-negative breast cancers

Identification of chemotherapy-induced transposable elements in triple-negative breast cancers

Ann-Sophie Gironne, co-supervision with Geneviève Deblois

Pairwise Molecular Representation Learning for Improved and Explainable Property Prediction

Pairwise Molecular Representation Learning for Improved and Explainable Property Prediction

Tom MacDougal, co-supervision with Anne Marinier

Identification of Potential Therapeutic Targets in AML Through Integration of Genomics Data and Small Molecule Screening Results

Identification of Potential Therapeutic Targets in AML Through Integration of Genomics Data and Small Molecule Screening Results

Safia Safa-tahar-henni, co-supervision with Brian Wilhelm

Investigate the Role of Transcriptional Regulation in Breast Cancer Heterogeneity Using Single Cell RNA Sequencing (scRNA-seq) Data

Investigate the Role of Transcriptional Regulation in Breast Cancer Heterogeneity Using Single Cell RNA Sequencing (scRNA-seq) Data

Marjolaine David, co-supervision with Sylvie Mader

Development of Automatic Risk Evaluation Systems for Acute Myeloid Leukemia from Gene Expression Data

Development of Automatic Risk Evaluation Systems for Acute Myeloid Leukemia from Gene Expression Data

Léonard Sauvé

Improving Peptide-Centric Mass Spectrometry with Deep Learning

Improving Peptide-Centric Mass Spectrometry with Deep Learning

Jérémie Zumer

Enhancing the Drug Discovery Process - Using Bayesian Inference for the Analysis of Dose-Response Experiments

Enhancing the Drug Discovery Process - Using Bayesian Inference for the Analysis of Dose-Response Experiments

Caroline Labelle, co-supervision with Anne Marinier

The Factorized Embeddings Model: Towards a Data-driven Cell Atlas

The Factorized Embeddings Model: Towards a Data-driven Cell Atlas

Assya Trofimov, co-supervision with Claude Perreault

The Non-Canonical Proteome Uniquely Populates the Proteome or Immunopeptidome

The Non-Canonical Proteome Uniquely Populates the Proteome or Immunopeptidome

Maria Virginia Ruiz Cuevas, co-supervision with Claude Perreault

Neural network development from pan-transcriptomic k-mer tables for RNA-Seq applications

Neural network development from pan-transcriptomic k-mer tables for RNA-Seq applications

Nicolas Jacquin