Speaker Profile

Ph.D., Founding Director of the Computational Medicine Center, Thomas Jefferson University

Dr. Rigoutsos is the Founding Director of the Computational Medicine Center (https://cm.jefferson.edu) at Thomas Jefferson University. Dr. Rigoutsos is the inaugural holder of the University’s Richard W. Hevner Chair in Computational Medicine. He is a Professor in the Department of Pathology with joint ap-pointments in the Department of Cancer Biology, and the Department of Biochemistry & Molecular Biolo-gy. Prior to joining Jefferson in 2010, Dr. Rigoutsos spent 18 years at IBM’s Thomas J. Watson Re-search Center, where he worked on Computational Biology. From 2000 to 2010, and in parallel with his IBM tenure, Dr. Rigoutsos was a Visiting Faculty at MIT’s Dept. of Chemical Engineering where he taught graduate-level classes and summer professional courses in Bioinformatics. The Rigoutsos lab studies post-transcriptional regulation by non-coding RNAs, including microRNAs, isomiRs, tRNAs, tRNA-derived fragments, pyknons, and, piRNAs. In recent years, the lab made several key contribu-tions to our understanding of the molecular biology of health disparities. Specifically, the lab was first to show that, in human tissues, the production of miRNA isoforms (isomiRs) and of tRNA-derived frag-ments (tRFs) is constitutive. Moreover, the lab showed that the abundances of isomiRs and tRFs de-pend on an individual’s sex, population origin, race/ethnicity, tissue, tissue state, and disease. These findings are important because isomiRs and tRFs are potent regulators of numerous genes and path-ways. The findings have important ramifications for implementing Precision Medicine, identifying accu-rate biomarkers, and designing targeted diagnostics and targeted therapeutics.

Session Abstract – PMWC 2018 Duke

Session Synopsis: The ability to generate Big Data is now within the reach of many who harness it through countless experiments in a variety of settings. The swift growth of private and public repositories of biological and clinical datasets is creating tremendous new opportunities for data-driven science and breakthrough discoveries in biology and medicine. At the same time, the sheer magnitude of these repositories puts increased demands on managing the data and orchestrating the rapidly evolving frameworks and applications.

This session will discuss how a team from Thomas Jefferson University coupled large collections of biological datasets with high-performance computing to generate unprecedented advances in Precision Medicine. The session will provide a unique opportunity to hear how Jefferson’s Computational Medicine Center has been using Big Data to guide their basic research work into disease disparities. The Jefferson team has generated strong evidence that “who we are” affects our predisposition for disease, how a disease will progress, how severe it will be, what the therapeutic options are, and other important factors. Specifically, they have shown that a person’s sex, race/ethnicity, and population origin affect the abundances of potent regulatory molecules and of the proteins these regulators control. These findings have already led to the design of a pan-cancer biomarker panel that can distinguish among 32 cancer types with high sensitivity and specificity. The Jefferson team’s efforts are laying the groundwork for revolutionary and powerful diagnostic techniques and novel approaches to therapy. Letting the data lead the way and using a high-performance data architecture as the catalyst has allowed this team to deliver groundbreaking research and generate invaluable insights that will boost Precision Medicine efforts.

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