Biomedical data integration with Zainab Doctor (nference)

Thank you for joining for Season 1 of the Data Pulse podcast. I hope you found the conversations over the past 6 months to be both enlightening and enjoyable. With chaos dominating the world this year, the podcast for me has been a grounding force, tapping into the power that lies in using data science to effectively tackle some of the grandest challenges in biomedicine. I have thoroughly enjoyed diving into the minds of peers and mentors who I deeply admire, building a resource for a shared vocabulary—through both the conversations and the podcast glossary, and growing a community of folks passionate about improving medicine through the lens of data. If you have a moment, check out the podcast website (linktr.ee/thedatapulse) to provide feedback on Season 1—I’d like to make it as relevant and useful for *you*! The link will also include an application to join the Data Pulse team as publicity head, audio lead, or script lead. Definitely apply if you are interested! Finally, if you enjoyed this podcast, I’d deeply appreciate a rating on whichever platform you listen. From the bottom of my heart, thank you for being a part of the Data Pulse community!

From single cells to international consortia and from striving despite fear to creating a "vector field" to inspire teams working in sync, Dr. Aviv Regev shares countless insights into how she has merged the worlds of computation and biomedicine, first at the Broad Institute and now at Genentech.

Rare diseases present a unique challenge in both diagnosis and treatment, given the small number of cases, often leaving them undiagnosed. I talk with Professor Isaac Kohane of Harvard Medical School about the Undiagnosed Diseases Network's efforts to catalogue and diagnose rare diseases, focusing on the role that data science--specifically, merging genotype and phenotype information, can play in bringing about hope to the families of affected individuals. We also discuss efforts to aggregate EHR data from hospitals across the world in order to better predict COVID-related symptoms and outcomes, faster than most healthcare systems have been able to. 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

Autism Spectrum Disorder remains a pressing yet elusive spectrum of conditions. In my conversation with Dennis Wall of Stanford University, we discuss the promise of technology- and augmented reality-based systems in both diagnosis and behavioral treatment for affected individuals. He shares the importance of understanding the context in which data is collected, as well as the ability for simple algorithms to yield actionable insights in the clinic. 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 my conversation with Greg Ryslik, previously Chief Data Officer and now Special Adviser to Celsius Therapeutics, we talk about single cell sequencing technologies and the nuance they enable with respect to identifying and targeting the cell populations that are responsible for driving diseases, as well as the machine learning frameworks he employs when approaching problems with large-scale datasets. 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

Working with humans poses significant challenges to acquiring robust and complete data, but also remarkable opportunity, as I learn in today's episode with Professor Manish Desai of Stanford University. We discuss inferring causality from longitudinal data, clinical trial and observational study considerations, and the intersection of statistics and medicine at large. 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

Natural language processing has long yielded exciting predictions from word-based knowledge. Through my conversation with Zainab Doctor, Translational Science Head of nference, I chat about how we can now use text as a lens into the biomedical world, as well as the ability to integrate diverse data types to synthesize knowledge of all scales and yield clinically actionable insights. 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

Can we create new biological therapies with machine-guided design? Today I chat with Peyton Greenside, Co-Founder and CSO of BigHat Biosciences, on using machine learning to design therapeutic proteins, the advantages of using "smart" data over "big" data, and the importance of interpretability. 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

Today I discuss with Vyas Ramanan, of Third Rock Ventures and Maze Therapeutics, the evolution of the field's understanding of genetic modifiers and the role they play in disease, how drug discovery works when attempting to recapitulate protective effects of certain mutations, and how the convergence of certain advances in statistics and genomic sequencing and perturbation tools enable pressure testing of hypotheses at an unprecedented scale. 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

How should one think about building "the new wave" of biopharma teams? Turns out we can rely on principles from existing domains. In this episode, I talk with Ankit Gupta, CTO and Co-Founder of Reverie Labs, on systematization as a foundation, and on optimizing properties of a drug using machine learning and software-oriented frameworks. 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