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This chapter explores innovative research in biomedical data science, featuring two pivotal studies that apply machine learning to significant biological inquiries. It emphasizes the advancements in AI methodologies and their potential to transform biological research in the near future.

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In this episode of the Cognitive Revolution, we hear from Siyu He, a postdoc at Stanford specializing in biomedical data science. Siyu discusses the implications and methods behind their recent AI-driven biological research papers, Squidiff and CORAL. The conversation explores the use of AI models to analyze complex cellular systems and disease mechanisms, focusing on transcriptome and tissue sample analyses. Squidiff aims to simulate cellular transcriptomes to predict outcomes of various conditions, significantly expediting traditionally lengthy and expensive biological experiments. CORAL Project extends this by integrating different levels of biological data, enabling a more comprehensive understanding of tissue structures and cellular interactions. The discussion also delves into the challenges of using synthetic data for validating AI models and the potential acceleration of scientific discoveries through AI in biomedical research. The episode encapsulates the interplay between AI and biology, highlighting the future possibilities and current limitations of this innovative research front.

Squidiff: https://www.biorxiv.org/content/10.1101/2024.11.16.623974v1.full.pdf

CORAL: https://www.biorxiv.org/content/10.1101/2025.02.01.636038v1.full.pdf

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CHAPTERS:

(00:00) About the Episode

(03:37) Introduction and Guest Welcome

(04:00) Setting the Big Picture Context

(04:37) Exploring the Squidiff and CORAL Papers

(08:31) Understanding Transcriptomes

(11:17) Single Cell RNA Sequencing Technology

(15:32) Motivation Behind Squidiff (Part 1)

(17:14) Sponsors: Oracle Cloud Infrastructure (OCI) | Shopify

(19:41) Motivation Behind Squidiff (Part 2)