DEOD: uncovering dominant effects of cancer-driver genes based on a partial covariance selection method | Zendy

Bayarbaatar Amgalan | Zendy; Hyunju Lee | Zendy

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DEOD: uncovering dominant effects of cancer-driver genes based on a partial covariance selection method

Author(s) -

Bayarbaatar Amgalan,

Hyunju Lee

Publication year - 2015

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btv175

Subject(s) - gene , covariance , selection (genetic algorithm) , breast cancer , computational biology , cancer , biology , genome , genetics , computer science , mathematics , statistics , artificial intelligence

The generation of a large volume of cancer genomes has allowed us to identify disease-related alterations more accurately, which is expected to enhance our understanding regarding the mechanism of cancer development. With genomic alterations detected, one challenge is to pinpoint cancer-driver genes that cause functional abnormalities.

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