Systematic assessment of analytical methods for drug sensitivity prediction from cancer cell line data

Pacific Symposium on Biocomputing

Volumn , Issue , 2014, Pages 63-74

  Jang, In Sock ^a   Neto, Elias Chaibub ^a   Guinney, Justin ^a   Friend, Stephen H ^a   Margolin, Adam A ^a  

^a SAGE BIONETWORKS   (United States)

Author keywords

Cancer cell lines; Machine learning; Pharmacogenomics; Predictive modeling

Indexed keywords

ARTIFICIAL INTELLIGENCE; CELL CULTURE; DISEASES; FORECASTING; GENE EXPRESSION; LEARNING SYSTEMS; OPEN SOURCE SOFTWARE; REGRESSION ANALYSIS; STATISTICS;

CANCER CELL LINES; DOSE-RESPONSE CURVES; MODELING GUIDELINES; PHARMACOGENOMICS; PREDICTIVE MODELING; RELATIVE PERFORMANCE; SYSTEMATIC ASSESSMENT; THERAPEUTIC STRATEGY;

OPEN SYSTEMS;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; BIOLOGICAL MODEL; BIOLOGY; DRUG RESISTANCE; GENE EXPRESSION PROFILING; GENETIC DATABASE; GENETICS; HUMAN; NEOPLASM; PHARMACOGENETICS; REGRESSION ANALYSIS; STATISTICS; TUMOR CELL LINE;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; CELL LINE, TUMOR; COMPUTATIONAL BIOLOGY; DATABASES, GENETIC; DRUG RESISTANCE, NEOPLASM; GENE EXPRESSION PROFILING; HUMANS; MODELS, GENETIC; NEOPLASMS; PHARMACOGENETICS; REGRESSION ANALYSIS;

EID: 84905489545     PISSN: 23356928     EISSN: 23356936     Source Type: Journal    
DOI: None     Document Type: Conference Paper

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