Jun Ding, McGill University
Meet: https://meet.google.com/niy-gtpk-sro
YouTube Stream: https://youtube.com/live/o1zzmvb0vXM?feature=share
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Abstract
Human diseases are driven by complex, dynamic changes in cellular states. While single-cell transcriptomics enables high-resolution profiling, a critical gap remains in computational tools capable of effectively modeling disease cells, progression trajectories, and enabling in silico drug discovery. To address this, we developed novel deep generative AI methods, built and learned from temporal and spatial single-cell multi-omics data, to construct "virtual" cell models and simulate disease dynamics.
We applied this framework to diverse complex diseases—including idiopathic pulmonary fibrosis (IPF), COVID-19, and multiple cancers. Our approach not only reconstructed disease dynamics with high fidelity but also facilitated virtual drug screening, identifying candidate therapeutic compounds that were experimentally validated. This demonstrates the framework's power to elucidate cellular mechanisms underlying disease progression, prioritize therapeutic interventions, and its broad applicability across distinct diseases. In this talk, I will present the design principles of these generative models, showcase their application to IPF and cancer datasets, and discuss how they empower in silico prediction and prioritization of therapeutic candidates.
Biography
Dr. Jun Ding is a Tenure-track Assistant Professor at McGill University, an affiliated member of RI-MUHC and Mila – Quebec AI Institute, and a Junior 2 FRQS Scholar in AI in health. His research focuses on developing deep generative neural networks to decode cellular dynamics from single-cell omics data, bridging AI and life sciences to uncover disease mechanisms and therapeutic strategies. Dr. Ding has published in leading journals, including Nature Biomedical Engineering, Nature Communications, Genome Research, Cell Stem Cell, and Genome Biology. His work, supported by CIHR and NSERC grants, advances AI-driven solutions for diagnostics and therapeutics in complex diseases.