In this episode, Dr. Jun Ding joins us to explore how artificial intelligence and machine learning are reshaping biomedical research at the cellular level. Dr. Ding is an Assistant Professor in the Division of Respiratory Medicine at the Research Institute of the McGill University Health Centre and leads the Ding Lab at McGill University's Meakins-Christie Laboratories.

Dr. Ding's research focuses on decoding cell dynamics across complex diseases by leveraging advanced single-cell technologies and computational biology. By developing machine-learning models such as probabilistic graphical models, Dr. Ding and his team aim to bridge massive omics datasets with actionable biological insight – paving the way for next-generation diagnostics and therapeutics…

Hit play to discover:

• Why understanding cell dynamics is critical to tackling complex diseases like cancer.

• How single-cell and spatial omics technologies are revealing previously hidden biological heterogeneity.

• The role of machine learning in modeling disease progression and identifying new drug targets.

To learn more about Dr. Ding and his work, visit The Ding Lab website!

🎧 Listen now on Apple Podcasts: http://apple.co/30PvU9C

Keep up with Dr. Ding socials here:

X: https://x.com/johnding86

🛍️ Recommended Books from this Episode

📖 Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again – An insightful exploration of how AI can restore empathy, precision, and humanity to modern medicine. 👉 Get it here

📘 The Gene: An Intimate History – A sweeping and deeply personal history of genetics, tracing how genes shape our past, present, and future. 👉 Check it out

📙 Cancer Systems Biology: Translational Mathematical Oncology – A highly specialized look at how mathematical models and systems biology are transforming cancer research and treatment. 👉 Explore it here

📕 Computational Methods for Single-Cell Data Analysis (Methods in Molecular Biology, 1935) – A technical deep dive into modern computational tools for analyzing single-cell data in biological research. 👉 Discover it here

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