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Research Article

Functional Impact of Missense Variants in BRCA1 Predicted by Supervised Learning

  • Rachel Karchin mail,

    To whom correspondence should be addressed. E-mail: karchin@karchinlab.org (RK); sali@salilab.org (AS)

    Affiliations: Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, United States of America, Institute of Computational Medicine, Johns Hopkins University, Baltimore, Maryland, United States of America

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  • Alvaro N. A Monteiro,

    Affiliation: Risk Assessment, Detection, and Intervention Program, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida, United States of America

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  • Sean V Tavtigian,

    Affiliation: International Agency for Research on Cancer, Lyon, France

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  • Marcelo A Carvalho,

    Affiliation: Risk Assessment, Detection, and Intervention Program, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida, United States of America

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  • Andrej Sali mail

    To whom correspondence should be addressed. E-mail: karchin@karchinlab.org (RK); sali@salilab.org (AS)

    Affiliations: Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, California, United States of America, California Institute for Quantitative Biomedical Research, University of California San Francisco, San Francisco, California, United States of America

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  • Published: February 16, 2007
  • DOI: 10.1371/journal.pcbi.0030026

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Posted by Qiong on 30 Mar 2012 at 20:15 GMT

This paper used the mutations of gene TP53 of tumor protein P53 as their training data and used four machine learning methods to train mode to predict the functional impact of missense variants in BRCA1. It's good, but from the statistical point of view, it should be better if we can use mutations from more than on protein as the training data.

No competing interests declared.