036: Dr. Josh Swamidass: Programming to Improve Health by Developing Algorithms for Medical Data

Finding new uses for existing medicines—drug “repurposing” or “repositioning”—has emerged as an attractive strategy for rescuing stalled pharmaceutical projects, finding therapies for neglected diseases, and reducing the time, cost and risk of drug development. At the same time that repurposing emerged as a drug development strategy, the data from hundreds of high-throughput screens (HTS)—the type of data once locked away in proprietary industry databases—became freely available to all. Small-molecule HTS, however, often misses active molecules because HTS projects are noisy and too narrow. Furthermore, it is difficult to mine public HTS repositories because HTS data is unorganized and its structure undocumented. In order to inform repurposing efforts with information from public screening data, Josh and his lab aim to reduce the number of missed active molecules in small-molecule HTS projects, to organize public HTS data by inferring and displaying their workflows, to identify known drugs likely to be active in the assays of public HTS projects, and to identify the pathways that modulate medically relevant phenotypes.