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Lo que permanece cierto en la investigación médica | Debashis Sahoo | TEDxSanDiegoSalon

Título original: Finding what stays true in medical research | Debashis Sahoo | TEDxSanDiegoSalon

5 nov 20269:36
TTEDx Talkshttps://www.youtube.com/watch?v=tEOqB4rCMz8Datos de la fuente
Sobre este vídeoTraducido por IAIdioma original: English

Debashis Sahoo analiza si los patrones estables en biología podrían hacer que la investigación científica sea más reproducible. Explica cómo la IA puede ayudar a descubrir patrones biológicos invariantes dentro de la complejidad de la biología humana. Según su planteamiento, estos hallazgos podrían revelar reglas que hagan la medicina más precisa y el conocimiento científico más confiable. La charla se presentó en un evento TEDxSanDiegoSalon.

En este vídeo
  • Métodos de IA para encontrar patrones biológicos invariantes
  • Relación entre patrones estables y ciencia reproducible
  • Implicaciones para la medicina de precisión
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Descripción de TEDx Talks

What if the key to making science more reproducible is hiding in the patterns that never change? In this talk, Debashis Sahoo explores how AI can help us uncover invariant biological patterns hidden within the enormous complexity of human biology—and how these discoveries could reveal the laws that make science more reliable, medicine more precise, and knowledge more trustworthy. Website: https://sites.google.com/view/debashis-sahoo Twitter/X: @sahoo00 LinkedIn: https://www.linkedin.com/in/sahoodebashis/ Facebook: https://www.facebook.com/debashis.sahoo #ReproducibleScience #AIinBiology #FutureOfMedicine #TEDxSanDiego #TEDx #MooresCancerCenter #Bioinformatics #Boolean Analysis #UCSD #AI Debashis Sahoo is a joint Associate Professor of Computer Science and Engineering and Pediatrics at the University of California, San Diego. He earned his B.Tech. from the Indian Institute of Technology, Kharagpur, and his M.S. and Ph.D. from Stanford University. His research bridges artificial intelligence and biology, developing logical and AI-driven methods to uncover invariant biological rules and predict patient responses. His work has led to advances in cancer and translational research, including the identification of CDX2 as a predictive biomarker in colon cancer (NEJM 2016). He is passionate about working at the intersection of disciplines and fostering collaborations among computational scientists, clinicians, and biologists to address real-world translational challenges. His group focuses on translating AI and logical modeling approaches into clinically actionable diagnostics and therapeutics. https://sites.google.com/view/debashis-sahoo This talk was given at a TEDx event using the TED conference format but independently organized by a local community. Learn more at https://www.ted.com/tedx

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