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The discoveries medical researchers and drug developers can make are constrained by the kinds of questions they can ask of their data. Unfortunately, when it comes to clinical trial data, or gene expression data, or population health data, it feels like you need a PhD in computer science just to know which questions are "askable" and how to frame them. This week, Harry talks with the founders of a startup working to solve that problem.

Tag.bio aims to make it possible for any worker in the life sciences sector—even if they don't have a PhD in computer science or data science—to interrogate their data quickly and automatically. The idea is to help them uncover trends or connections in their data that would otherwise require months of work and help from a data scientist or a data engineer.

The company was founded in 2014 as a spinoff from the University of California, San Francisco Cancer Center. That’s where co-founder Jesse Paquette first invented a system that let oncology researchers ask guided questions of their data without help from a bioinformatics expert. Now Paquette is Tag.bio’s chief science officer, and in this episode, he's joined by Tag.bio CEO Tom Covington to talk about how the startup's technology works and why easier access to data is critical to faster progress in drug discovery and to the whole idea of precision medicine.

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Transcript

Harry Glorikian: I’m Harry Glorikian, and this is MoneyBall Medicine, the interview podcast where we meet researchers, entrepreneurs, and physicians who are using the power of data to improve patient health and make healthcare delivery more efficient. You can think of each episode as a new chapter in the never-ending audio version of my 2017 book, “MoneyBall Medicine: Thriving in the New Data-Driven Healthcare Market.” If you like the show, please do us a favor and leave a rating and review at Apple Podcasts.

Harry Glorikian: In healthcare and drug discovery, everybody’s got data. Knowing what to do with your data and how to get value out of it is the trick. That’s what we’ve spent the last 60-something episodes of this podcast talking about. 

Unfortunately, when it comes to clinical trial data, or gene expression data, or population health data, it feels like you need a PhD in computer science just to know what questions to ask and how to ask them.

But there’s a startup in San Francisco that aims to break down that barrier and make it possible for any worker in the life sciences sector to interrogate their data quickly and automatically. The idea is to