A machine learning approach for genome-wide prediction of morbid and druggable human genes based on systems-level data

BMC Genomics

Volumn 11, Issue SUPPL. 5, 2010, Pages

  Costa, Pedro R ^a   Acencio, Marcio L ^a   Lemke, Ney ^a  

^a SÃO PAULO STATE UNIVERSITY UNESP   (Brazil)

Author keywords

[No Author keywords available]

Indexed keywords

ACCURACY; ANALYTIC METHOD; AREA UNDER THE CURVE; ARTICLE; CALCULATION; CELL MEMBRANE; CELLULAR DISTRIBUTION; ESCHERICHIA COLI; GENE EXPRESSION PROFILING; GENE INTERACTION; GENOME ANALYSIS; HUMAN; INTERMETHOD COMPARISON; MACHINE LEARNING; PREDICTION; SACCHAROMYCES CEREVISIAE; SCORING SYSTEM; TRANSCRIPTION REGULATION; ARTIFICIAL INTELLIGENCE; BIOLOGY; DRUG DEVELOPMENT; GENETIC DISORDER; GENETICS; GENOMICS; METABOLISM; METHODOLOGY;

PROTEIN;

ARTIFICIAL INTELLIGENCE; COMPUTATIONAL BIOLOGY; DRUG DISCOVERY; GENETIC DISEASES, INBORN; GENOMICS; HUMANS; PROTEINS;

EID: 78651467817     PISSN: None     EISSN: 14712164     Source Type: Journal    
DOI: 10.1186/1471-2164-11-S5-S9     Document Type: Article

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