How CytoReason is Bridging the Data Insight Gap to Accelerate Healthcare Breakthroughs - Ep. 276

Hello, and welcome to the NVIDIA AI podcast. I'm your host, Noah Kravitz. You've heard of language models, video models, reasoning models, and foundational models. And here on the podcast, we've talked a lot about health care specific AI models for things like protein structure prediction. Well, today, we're exploring disease models. The CytoReason disease model is a comprehensive model of human diseases that models and compares treatments and patient groups, helping researchers of all levels make data driven decisions across the drug development life cycle. That brief description doesn't really do justice to what disease models and CytoReason as a company are all about, but our guest is here to help. Shai Shen Or is cofounder and chief scientist at CytoReason and professor of systems immunology and precision medicine at the Technion, the Israel Institute of Technology. Shai is here to tell us all about CytoReason, how it got started, why the technology is so important, and what they're trying to do. And we're grateful to have you here. So, Shai, welcome, and thanks for joining the AI podcast. Oh, thanks, Nava. Pleasure to be here. Thank you for inviting me to speak about my favorite subject, I guess. Please. And just take it take it right from there. I don't even need a a more pointed question. Tell us about tell us about your favorite subject. Maybe start with your background a little bit, and then tell us how CITORreason came to be and what it's all about. Sure. So, yeah, I'll go back. I guess, at this point, I can can think of myself a bit of a dinosaur in the face of, doing computational biology data science. I started back in the, I guess, late twentieth century, as they say, with the idea where basically, you know, we're just starting the human genome is getting sequenced and the realization, that biology is making a leap from a one tube, one result, type field to one tube, a million results type field. Okay. Right? And suddenly, there's room for, you know, what evolved to what I think we now we think about as data science and AI in the context of of, medicine and, and kind of life sciences and health care. And and and for me, that, discovery of falling in love in biology, you can do. I was kind of doing a lot of stuff around AI in the late nineties as they say. Hey. Very different space. Eons ago. Yeah. Yes. But discovering that you can actually use the same kind of the the same type of thinking, but in a space such as life sciences and health care, was to me, profound and kind of changed my life course. Realizing that in this space, actually, not only, you know, is the data interesting and there's a good, I think, humanitarian cause, but also are the that the AI challenges are profound because this data is is I I often call it deep more …