AI Tool Identifies Aggressive Cancer Cells
A new AI tool developed by Jun Ding identifies the most aggressive cancer cells and predicts patient outcomes.
Jun Ding
Jun Ding, PhD
Assistant Professor, Department of Medicine, McGill University
Research Theme: Cell dynamics in biological processes and diseases (pulmonary, developmental, cancer); machine learning approaches to analyze, model, and visualize single-cell omics data
Keywords: cell dynamics • single cell omics technologies • machine learning • computational models
View Jun’s recent posts and news below.
AI is Transforming Drug Discovery – Jun Ding Featured by BBC
Work by Jun Ding featured by the BBC highlights how AI is accelerating treatment discovery for complex diseases
CIHR Project Grant Results – Fall 2025
Congratulations to all RESP members who were funded in the Fall 2025 CIHR Project Grant Competition!
Jun Ding’s AI4Health Works Featured by Anaconda, a Global AI Leader
Jun Ding and his team are transforming drug discovery with AI, earning global recognition from Anaconda for their AI4Health work
AI Tool DOLPHIN Uncovers Hidden Disease Markers Inside Single Cells
Jun Ding and team have developed DOLPHIN, an AI tool that goes beyond gene-level analysis to detect overlooked disease markers
UNAGI: Simulating Disease to Discover New Treatments with AI
Jun Ding and his team develop UNAGI, a deep generative AI tool to decode cellular dynamics and model disease
2025-2026 FRQS Salary Awards
Congratulations to Jun Ding, Bryan Ross, and Nicole Ezer for their FRQS salary award!
New AI Tool Reveals Hidden Clues to Disease Outcomes
RAMEN is a novel AI tool that uncovers hidden links between symptoms and outcomes in diseases like COVID-19, COPD, and sepsis – advancing precision medicine.
CIHR Project Grant Results – Fall 2024
Congratulations to all RESP members who were funded in the Fall 2024 CIHR Project Grant Competition!
Nature Commentary Highlights the Work of Jun Ding
Nature commentary highlights scSemiProfiler, UNAGI, and CellAgentChat, new computational tools developed by Jun Ding