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Evaluation and integration of cancer gene classifiers: identification and ranking of plausible drivers.


ABSTRACT: The number of mutated genes in cancer cells is far larger than the number of mutations that drive cancer. The difficulty this creates for identifying relevant alterations has stimulated the development of various computational approaches to distinguishing drivers from bystanders. We develop and apply an ensemble classifier (EC) machine learning method, which integrates 10 classifiers that are publically available, and apply it to breast and ovarian cancer. In particular we find the following: (1) Using both standard and non-standard metrics, EC almost always outperforms single method classifiers, often by wide margins. (2) Of the 50 highest ranked genes for breast (ovarian) cancer, 34 (30) are associated with other cancers in either the OMIM, CGC or NCG database (P?

SUBMITTER: Liu Y 

PROVIDER: S-EPMC4650817 | biostudies-other | 2015 May

REPOSITORIES: biostudies-other

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Evaluation and integration of cancer gene classifiers: identification and ranking of plausible drivers.

Liu Yang Y   Tian Feng F   Hu Zhenjun Z   DeLisi Charles C  

Scientific reports 20150511


The number of mutated genes in cancer cells is far larger than the number of mutations that drive cancer. The difficulty this creates for identifying relevant alterations has stimulated the development of various computational approaches to distinguishing drivers from bystanders. We develop and apply an ensemble classifier (EC) machine learning method, which integrates 10 classifiers that are publically available, and apply it to breast and ovarian cancer. In particular we find the following: (1  ...[more]

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