Optimizing Neural Networks for Biological Predictions

This chapter explores the optimization of neural networks for predicting similarities among biological nodes, emphasizing techniques for validating embeddings. It highlights the importance of clustering in uncovering biological functions and drug targets while transitioning to a message about innovative AI applications.

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Nathan hosts Professor Michael Levin and Staff Scientist Dr. Leo Pio Lopez from Tufts University in this episode of The Cognitive Revolution. They discuss their groundbreaking paper that combines biological datasets into a unified network model of disease using advanced embedding techniques. The conversation covers the technical details of their work, including a predicted link between GABA and melanoma, and explores broader topics in AI for biology. From multi-scale intelligence in biological systems to the future of human enhancement and digital life, this thought-provoking episode reminds us of the rapidly approaching future and the work needed to prepare for it.

Links to the papers discussed in the episode:

1) Universal Multilayer Network Embedding Reveals a Causal Link Between GABA Neurotransmitter and Cancer

2) https://www.nature.com/articles/s42003-024-06037-4.pdf

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CHAPTERS:

(00:00:00) Teaser

(00:00:52) About the Show

(00:01:15) About the Episode

(00:04:45) Intro

(00:06:32) Multi-Layer Networks

(00:10:41) Network Embedding (Part 1)

(00:16:50) Sponsors: Notion

(00:18:30) Network Embedding (Part 2)

(00:18:30) GABA and Cancer

(00:24:08) AI for Biology

(00:29:59) Emergent Cognition (Part 1)