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

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

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Dr. Joshua Swamidass is an Assistant Professor in the Department of Immunology and Pathology and the Division of Laboratory and Genomic Medicine at Washington University in St. Louis. He received his Masters and PhD in Information and Computer Sciences from the University of California, Irvine and also went on to get his MD from the University of California, Irvine. Joshua had previously served as a Visiting Scientist at the Broad Institute of Harvard and MIT in the Chemical Biology Program and at Pfizer while he was a professor at Washington University.

Josh and his team design and apply novel algorithms to solve medically important problems in the life sciences. Within this area, his core interest is in designing algorithms to help discover new types of medicines and new uses for existing medicines. This work is currently being conducted in the context of several key collaborations and includes: high throughput screening analysis, machine learning and probabilistic inference, and experimental design using economic and decision theory.

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.

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