Conventional drug discovery ignores the spatial relationships between cells when assessing the effects of drugs. I talk with Imran Haque, VP of Data Science at Recursion Pharmaceuticals, about their data-first approach that combines "cell painting" with deep learning to assess changes in cellular morphology, thus providing a crucial lens into the molecular wirings of a given drug's mechanism of action. We discuss his team's work in repurposing therapies to tackle COVID-19 and what it takes to successfully merge deep learning with cellular images in a way that yields therapies.

Check out the glossary of terms, definitions, and resources (and get a sneak peak of the future conversations lined up!) here: bit.ly/datapulse-glossary

Genomics has been a largely homogeneous field--both with respect to the researchers and the individuals whose data has been collected. Carlos Bustamante, Professor at Stanford University and F-Prime Venture Partner, whose work studies populations of diverse ancestry, claims that COVID has brought these disparities to light and that we have a unique opportunity to sequence the next generation of babies as one step towards more equitable precision medicine and drug development. He also discusses how the patient, as the healthcare consumer, will soon govern what solutions are adopted, including digital medicine and continuous tracking.

Check out the glossary of terms, definitions, and resources (and get a sneak peak of the future conversations lined up!) here: bit.ly/datapulse-glossary

In 2016, a team at Google Health led by Product Manager Lily Peng accurately predicted diabetic retinopathy in patients solely from images of their eyes. Today I chat with Lily about this work in diagnosing diabetic retinopathy by tapping into advances in deep learning, what factors determine whether a medical problem is well-suited to machine learning tasks, and the iterative process to achieve model performance in the context of a clinical environment. We also discuss tactics to effectively deploy such technologies in resource-poor regions in a sustainable way.

Check out the glossary of terms, definitions, and resources (and get a sneak peak of the future conversations lined up!) here: bit.ly/datapulse-glossary

What does a hybrid team working at the interface of machine learning and biomedicine look like? In this episode, I chat with Daphne Koller, Founder and CEO of insitro, about a data-first approach to drug discovery, building the systems that enable large-scale learning, and the importance of a bilingual culture in "digital biology".

Check out the glossary of terms, definitions, and resources (and get a sneak peak of the future conversations lined up!) here: bit.ly/datapulse-glossary

Welcome to The Data Pulse, a podcast that explores the growing role that data science and computation play in the latest biotechnology and biomedical innovations. Join me as I speak with pioneers in the space, to learn more about how they use data-driven tactics to advance biology and medicine, to understand the principles that guide their work, and to better equip yourself with the language and thinking essential to effectively contributing to this space.

I’m your host, Anika Gupta, a PhD student in Bioinformatics at Harvard and the Broad Institute. To introduce the podcast, I'm joined by Anthony Philippakis, Chief Data Officer of the Broad Institute and a Venture Partner at GV (formerly Google Ventures). We discuss his unique journey and roles, why this intersection is promising, and what the key ingredients for innovation in this space are.

To bring you each episode, I’ve been fortunate to tap into the wisdom of many of my mentors and peers who are trailblazing at this convergence. In addition to my conversations, I’ve created a glossary of terms, definitions, and links to references (bit.ly/datapulse-glossary) that my guests mention.