An Active Learning Framework Improves Tumor Variant Interpretation.

Journal: Cancer research
Published Date: Aug 3, 2022

Abstract

A novel machine learning approach predicts the impact of tumor mutations on cellular phenotypes, overcomes limited training data, minimizes costly functional validation, and advances efforts to implement cancer precision medicine.

Authors

  • Alexandra M Blee
    Department of Biochemistry and Center for Structural Biology, Vanderbilt University, Nashville, Tennessee.
  • Bian Li
    Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, Connecticut, United States of America.
  • Turner Pecen
    John B. Little Center of Radiation Sciences, Department of Environmental Health, Harvard T. H. Chan School of Public Health, Boston, Massachusetts.
  • Jens Meiler
    Department of Chemistry, Vanderbilt University, Nashville, TN, United States.
  • Zachary D Nagel
    John B. Little Center of Radiation Sciences, Department of Environmental Health, Harvard T. H. Chan School of Public Health, Boston, Massachusetts.
  • John A Capra
    Department of Biological Sciences and Vanderbilt Genetics Institute, Vanderbilt University, Nashville, Tennessee, United States of America.
  • Walter J Chazin
    Department of Biochemistry and Center for Structural Biology, Vanderbilt University, Nashville, Tennessee.

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