Speaker Profile

Ph.D., CEO, Cofactor Genomics

Biography
Jarret Glasscock is a geneticist and computational biologist who leads the team at Cofactor Genomics, a tools and diagnostics company leveraging the power of RNA to diagnose disease. Prior to founding Cofactor Genomics, Jarret was faculty in the Department of Genetics at Washington University and part of The Genome Institute. While at WashU, he was involved in the Human Genome Project, published the first Cancer Genome, lead the Institute’s Computational Biology Group, and was part of the Institute’s Technology Development Group tasked with characterizing the first RNA-seq experiments on early instruments such as 454 and Illumina/Solexa(Serial #1). Jarret’s work to leverage signals in RNA to propel Precision Medicine Initiatives has been covered by Genetic Engineering News (GEN), Tech Crunch, and Wired Magazine. He is a member of the Personalized Medicine Coalition, International Society for Computational Biology, and is a Y Combinator Alum.


AI and Data Sciences Showcase: Cofactor Genomics
Cofactor Genomics uses Predictive Immune Modeling to capture the complexity of disease and build powerful multidimensional biomarkers. Cofactor has spent years pioneering the tools to build an RNA-based database of Health Expression Models, unlocking precision medicine and clinical diagnostics.

Leveraging All Data with Multidimensional RNA Models
Precision medicine in an era of big data requires reducing complexity to meaningful information. The field of Predictive Immune Modeling enables a patient’s immune profile to be used as a powerful diagnostic tool leveraging immune Health Expression Models.

 Session Abstract – PMWC Silicon Valley


The PMWC 2020 AI Company Showcase will provide a 15-minute time slot for selected AI companies to present their latest technologies to an audience of leading investors, potential clients, and partners. We will hear from companies building technologies that expedite the pre-clinical and clinical drug discovery and development process, accelerate patient diagnosis and treatment, or develop scalable systems framework to make AI and deep/machine learning a reality.