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Amrita Basu, PhD

Title(s)Assistant Professor, Surgery
SchoolSchool of Medicine
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    Collapse Biography 
    Collapse Education and Training
    Broad Institute of Harvard and MIT, Cambridge, MAPostdoctoral FellowComputational Chemical Biology
    Rockefeller University, Tri-Institutional Computational Biology Program, New York, New YorkPh.D.Computational Biology
    Cornell University, Ithaca. NY B.S.Electrical Engineering
    Collapse Awards and Honors
    2016  - 2017White House Presidential Innovation Fellow
    2013Sage Bionetworks Young Investigator Award
    2019  - 2020Interstellar Award (NYAS/Japan Center for Medical Research and Development)

    Collapse Overview 
    Collapse Overview
    Development of computational models for early detection of cancer lesions and progression to metastatic disease can help discriminate between high and low cancer risk profiles. Therefore, we seek to exploit diverse, high-throughput genomic and clinical data to understand the molecular networks underlying fundamental cellular processes that can eventually stratify patients by non-traditional underpinnings, including transcriptional regulation, epigenetic signaling, and chemosensitivity. Our algorithmic methods draw on machine learning, a computational field concerned with learning accurate, predictive models from noisy and high-dimensional data.

    Collapse Research 
    Collapse Research Activities and Funding
    P01CA210961Sep 8, 2017 - Aug 31, 2022
    Role: Co-Investigator

    Collapse Bibliographic 
    Collapse Publications
    Publications listed below are automatically derived from MEDLINE/PubMed and other sources, which might result in incorrect or missing publications. Researchers can login to make corrections and additions, or contact us for help.
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    1. Basu A, Mitra R, Liu H, Schreiber SL, Clemons PA. RWEN: response-weighted elastic net for prediction of chemosensitivity of cancer cell lines. Bioinformatics. 2018 Oct 01; 34(19):3332-3339. PMID: 29688307.
      View in: PubMed
    2. Basu A, Bodycombe NE, Cheah JH, Price EV, Liu K, Schaefer GI, Ebright RY, Stewart ML, Ito D, Wang S, Bracha AL, Liefeld T, Wawer M, Gilbert JC, Wilson AJ, Stransky N, Kryukov GV, Dancik V, Barretina J, Garraway LA, Hon CS, Munoz B, Bittker JA, Stockwell BR, Khabele D, Stern AM, Clemons PA, Shamji AF, Schreiber SL. An interactive resource to identify cancer genetic and lineage dependencies targeted by small molecules. Cell. 2013 Aug 29; 154(5):1151-1161. PMID: 23993102.
      View in: PubMed
    3. Mitchell L, Huard S, Cotrut M, Pourhanifeh-Lemeri R, Steunou AL, Hamza A, Lambert JP, Zhou H, Ning Z, Basu A, Côté J, Figeys DA, Baetz K. mChIP-KAT-MS, a method to map protein interactions and acetylation sites for lysine acetyltransferases. Proc Natl Acad Sci U S A. 2013 Apr 23; 110(17):E1641-50. PMID: 23572591.
      View in: PubMed
    4. Basu A. Computational prediction of lysine acetylation proteome-wide. Methods Mol Biol. 2013; 981:127-36. PMID: 23381858.
      View in: PubMed
    5. Basu A, Rose KL, Zhang J, Beavis RC, Ueberheide B, Garcia BA, Chait B, Zhao Y, Hunt DF, Segal E, Allis CD, Hake SB. Proteome-wide prediction of acetylation substrates. Proc Natl Acad Sci U S A. 2009 Aug 18; 106(33):13785-90. PMID: 19666589.
      View in: PubMed
    6. Whitcomb SJ, Basu A, Allis CD, Bernstein E. Polycomb Group proteins: an evolutionary perspective. Trends Genet. 2007 Oct; 23(10):494-502. PMID: 17825942.
      View in: PubMed
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