Translating Proteomics Episode 6 – The Future of AI in Biomedicine

‘Translating Proteomics’ explores the science of proteomics and its growing impact on biological research, biomarker discovery, drug development, food and energy security, and a range of other timely topics. The goal of these conversations is to expose you to important issues in proteomics, deepen your love of science, and prompt you to question assumptions about what may be possible.

Your hosts are Drs. Parag Mallick and Andreas Huhmer of Nautilus Biotechnology. Parag is an Associate Professor at Stanford University whose lab performs systems biology studies that drive precision medicine approaches for cancer diagnosis and treatment. Andreas is a veteran scientist whose industry work has supported thousands of proteomics researchers by helping to bring the latest mass spec technologies into their labs.

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 (You’re here)
Ep 7 – Harnessing Proteoforms to Understand Life’s Complexity
Ep 8 – Expanding the Druggable Universe with Proteomics

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Episode 6 – The Future of AI in Biomedicine

AI might be the biggest buzz word of the decade, but the buzz is warranted in terms of its practical potential in biological research. In this episode of Translating Proteomics, Parag and Andreas discuss some of the early wins for AI in biology, practical ways AI can be applied to biology research in the near term, challenges in that application, and how proteomics researchers in particular can use AI to advance their work.

Chapters:

• 00:00 – Why now is the time to apply AI to biomedicine
• 05:28 – Difficulties and potential solutions when applying AI to biology
• 14:20 – How AI will impact the study of proteins
• 19:34 – Risks of AI in biomedicine

Some key points of discussion:

• AI has tremendous potential in biomedicine
• AI can help us recognize patterns in biological data, but we need more data to maximize usefulness
• We can better leverage AI in biomedicine if biological data and data sharing are standardized

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