The Algorithm for Precision Medicine :: Jane Street
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The Algorithm for Precision Medicine
Matt Might
Hugh Kaul Precision Medicine Institute
Precision medicine promises to deliver ultra-personalized care by casting medicine as an optimization problem: identifying the best possible treatment with respect to all available data.
A slew of recent advances in biology, starting with the ability to sequence the human genome, have caused an explosion in the amount of data one can collect on a single patient and a similar explosion in the complexity of reasoning about this data in order to solve this optimization problem. Computational support for the practicing physician is no longer an option.
This talk covers precision medicine from the ground up for computer scientists — through a personal journey from programming languages research into academic medicine. It will demonstrate progress to date, including the now-routine use of relational programming in miniKanren to identify personalized treatments for patients with some of the rarest and most challenging diseases in the world.
Transcript
Matt Might has been the director of the Hugh Kaul Precision Medicine Institute at the University of Alabama since 2017. At UAB, Matt is the Hugh Kaul Kaul Endowed Chair of Personalized Medicine, a professor of internal medicine, and a professor of computer science.
Thank you. It’s a pleasure to be here and to present to you all.
I know you’re deeply interested in medical topics. Turns out, I used to be one of you guys, I spent most of my life as a computer scientist, more specifically as a functional programmer and academic and functional programming languages. I got to give you my standard disclaimer on this sort of stuff. It’s slightly modified for this audience, and you might even consider it more of a confession in that sense.
This is actually a medical talk. I really mostly do, at least what I know how to do, is programming languages and functional programming. Actually, I even try to give a sense of what my work has been like over the years. If you look at the split along object oriented versus functional, it’s almost all functional in terms of the kinds of research I used to do. If you look at the kind of stuff I published up until quite recently, it was all PL. Nothing in medicine.
All that is to say is that I really don’t have any medical training and if I give you medical advice in this talk, don’t take it. Yeah, this is what happens if you try to remove ants from your backyard with gas and forget about what happens when you get an air fuel mixture. I guess the ants are gone. Relocated or something like that.
I also want to make an observation. This is a talk really about the fusion of CS and biology. This fusion is not even remotely new. It’s been going on really since the very beginning. Some of you might be familiar with the famous biologist Alan Turing. This is true. He’s actually a biologist. He just didn’t realize it. In fact, to prove it, go to Google Scholar and look at his publications, his top publication is in biology, more citations than everything else.
[inaudible 00:02:11] it’s his one paper in biology, but it’s a foundational paper in biology and he’s using computational modeling, essentially reaction diffusion systems to show how do complex organisms evolved from single cells. How do you get the interesting patterns you see in nature, like say, the stripes on a zebra or the shapes of a leaf?
Even before we knew what DNA was, he got it right. He found the model that actually predicts how this works in nature. He’s still widely regarded within mathematical biology as one of the founders of the field.
So I’m going to make some claims today. I will claim that data is the greatest drug of the 21st century. I really do think that when we look back from the end of the century, we will believe that this was the best thing we ever used for improvement in human health. I’ll claim that this emerging field of precision medicine which I now work delivers data as a drug. That’s really what we do. That’s because precision medicine is a process. It’s a step by step process. I’ll present an algorithm for doing precision medicine.
And I’m also going to make another claim. For those of you who spend enough time drinking the FP Kool-Aid to play with proofs of inference rules and things like that. I’m also going to claim that for precision medicine to work in practice, you have to generate proofs. By that I mean, every time you make a recommendation to a physician, you have to tell them why you think it’s a good idea. You can’t just say, “My algorithm said so.”...