Gene therapies have emerged as a promising new modality for curing genetically-defined diseases; however, the naturally occuring variation remains limited. I chat with Sam Sinai and Jeff Gerold, Co-Founder/Lead ML Scientist and Head of Data Science, respectively, of Dyno Therapeutics about the role machine learning can play in better identifying and designing gene therapy vectors for a suite of traditional bottlenecks in the gene therapy development workflow, as well as the importance of building an environment where cross-talk between different domains is seamless.

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