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Aviad Tsherniak

Publications

Tsherniak A, Vazquez F, et al. Defining a cancer dependency map. Cell. 2017;170:564-576.

Meyers RM, Bryan JG, et al. Computational correction of copy-number effect improves specificity of CRISPR-Cas9 essentiality screens in cancer cells. bioRxiv.

Rosenbluh J, Xu H, et al. Complementary information derived from CRISPR Cas9 mediated gene deletion and suppression. Nature Communications. 2017;8:15403.

Aguirre AJ, Meyers RM, et al. Genomic copy number dictates a gene-independent cell response to CRISPR-Cas9 targeting. Cancer Discov. 2016;6(8):914–929.

Kryukov, G.V., Wilson, et al. MTAP deletion confers enhanced dependency on the PRMT5 arginine methyltransferase in cancer cells. Science. 2016;351(6278):1214-1218.

Shao DD, Tsherniak A, et al. ATARiS: computational quantification of gene suppression phenotypes from multisample RNAi screens. Genome Res. 2013;23:665–678.

Aviad Tsherniak

Aviad Tsherniak leads the Cancer Data Science group at the Broad Institute of MIT and Harvard. He is interested in developing computational methods enabling the identification of therapeutically relevant cancer vulnerabilities that would lead to new drug development efforts and a greater understanding of the biological mechanisms driving cancer.

Tsherniak works closely with Cancer Program director and Broad chief scientific officer Todd Golub and institute member William Hahn on the scientific planning and execution of the Cancer Dependency Map, an effort to exhaustively characterize cancer vulnerabilities and the molecular biomarkers defining them across many cancer types. In this role, he directs the efforts to integrate ‘omics data with functional screening datasets, such as genome-scale CRISPR-Cas9 knockout data generated as part of Project Achilles and multiplexed small-molecule screens conducted using the PRISM platform.

The Cancer Data Science group, under Tsherniak’s leadership, specializes in using machine learning techniques to build predictive models that can predict tumor cells’ vulnerabilities from molecular profiling data, even in small sample size conditions. The group’s ultimate goal is to make the precision cancer medicine vision a reality.

Prior to coming to the Broad Institute in 2007, Tsherniak worked in IBM Research Labs’ Machine Learning group to solve problems he describes as computationally similar to those he works on now, but applied to misbehaving IBM servers instead of tumors. He holds B.Sc. and M.Sc. degrees in math and computer science from Tel Aviv University.

Contact Aviad via email at [first name] at broadinstitute.org.

July 2017