Amrita Basu, PhD
Assistant Professor
Surgery
School of Medicine

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.

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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.

Another area of research includes developing standardized methods and measures to integrate drug toxicity, quality of life, and efficacy measures for breast cancer patients. We are building infrastructure and tools to support patient-reported outcomes collection and downstream analysis and visualization.

Awards

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  • Burroughs Wellcome Fund Innovation in Regulatory Science Award, 2021-2026
  • Interstellar Award (New York Academy of Sciences/Japan Center for Medical Research and Development), 2019-2020
  • White House Presidential Innovation Fellow, 2016-2017
  • Sage Bionetworks Young Investigator Award, 2013

Education & Training

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  • Postdoctoral Fellow Computational Chemical Biology Broad Institute of Harvard and MIT
  • B.S. Electrical Engineering Cornell University
  • Ph.D. Computational Biology Rockefeller University, Tri-Institutional Computational Biology Program

Grants and Projects

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Publications (19)

Top publication keywords:
ProteomeLysineNeoplasmsAcetylationComputational BiologyG-QuadruplexesAntineoplastic AgentsHealth Information InteroperabilityX-linked Nuclear ProteinDatabases, PharmaceuticalDrug DiscoveryBreast NeoplasmsQuality of LifeHistonesData Curation

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