An Active Learning Framework Improves Tumor Variant Interpretation

Alexandra M Blee; Bian Li; Turner Pecen; Jens Meiler; Zachary D Nagel; John A Capra; Walter J Chazin

doi:10.1158/0008-5472.CAN-21-3798

An Active Learning Framework Improves Tumor Variant Interpretation

Cancer Res. 2022 Aug 3;82(15):2704-2715. doi: 10.1158/0008-5472.CAN-21-3798.

Authors

Alexandra M Blee^#¹, Bian Li^#², Turner Pecen³, Jens Meiler^{4

5}, Zachary D Nagel³, John A Capra⁶, Walter J Chazin^{1

4}

Affiliations

¹ Department of Biochemistry and Center for Structural Biology, Vanderbilt University, Nashville, Tennessee.
² Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee.
³ John B. Little Center of Radiation Sciences, Department of Environmental Health, Harvard T. H. Chan School of Public Health, Boston, Massachusetts.
⁴ Department of Chemistry and Center for Structural Biology, Vanderbilt University, Nashville, Tennessee.
⁵ Institute for Drug Discovery, Leipzig University Medical School, Leipzig, Germany.
⁶ Bakar Computational Health Sciences Institute and Department of Epidemiology and Biostatistics, University of California, San Francisco, California.

^# Contributed equally.

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.

Publication types

Research Support, Non-U.S. Gov't
Research Support, N.I.H., Extramural

MeSH terms

Humans
Machine Learning*
Mutation
Neoplasms* / genetics
Neoplasms* / therapy
Precision Medicine

Abstract

Publication types

MeSH terms

Grants and funding