Translating Proteomics Episode 13 – AI and Biotech – The Promise and the Pitfalls with Vijay Pande and Matt McIlwain

Chapters

00:00 – Introduction
04:37 – How did Vijay and Matt get into AI and ML
07:33 – The importance of structured data, advances in compute, and algorithmic advances in driving the boom in machine learning
18:44 – The Intersection of AI and biology
21:57 – The evolution of biological models
31:55 – The Complexity of biological data
39:42 – Ways founders and biotech startups are using AI
43:25 – Favorite/Impactful applications of AI/ML
47:00 – AI for experimental design
50:13 – The future of AI in bio/health

Resources

• Learn about various types of machine learning on IBM’s website
• Learn about autoencoders on IBM’s website
• Learn more about transformers on NVIDIA’s blog
• Translating Proteomics Episode 6 – The Future of AI in Biomedicine

All Translating Proteomics episode links

Ep 1 – Poised for a Proteomics Breakthrough
Ep 2 – Putting Proteomics to Work
Ep 3 – Biology in Space and Time
Ep 4 – Single-protein Biomarkers Don’t Cut It
Ep 5 – Why the Dogma around Biology’s Central Dogma Is Wrong
Ep 6 – The Future of AI in Biomedicine
Ep 7 – Harnessing Proteoforms to Understand Life’s Complexity
Ep 8 – Expanding the Druggable Universe with Proteomics
Ep 9 – Are proteins a myth? With Special Guest Neil Kelleher
Ep 10 – Protein Function 201 with Kathryn Lilley
Ep 11 – Plasma Proteomics – The Dream and the Nightmare
Ep 12 – A New Era in Alzheimer’s Research with Sarah DeVos
Ep 13 – AI and Biotech – The Promise and the Pitfalls with Vijay Pande and Matt McIlwain