Machine learning-based prediction of drug-drug interactions by integrating drug phenotypic, therapeutic, chemical, and genomic properties.

Journal of the American Medical Informatics Association : JAMIA

Volumn 21, Issue e2, 2014, Pages

  Cheng, Feixiong ^a   Zhao, Zhongming ^a  

^a VANDERBILT UNIVERSITY SCHOOL OF MEDICINE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DRUG;

ARTICLE; ARTIFICIAL INTELLIGENCE; BAYES THEOREM; CHEMICAL STRUCTURE; CHEMISTRY; DECISION TREE; DRUG INTERACTION; HUMAN; PHARMACOKINETICS; RECEIVER OPERATING CHARACTERISTIC; STATISTICAL MODEL; SUPPORT VECTOR MACHINE; THEORETICAL MODEL;

ARTIFICIAL INTELLIGENCE; BAYES THEOREM; DECISION TREES; DRUG INTERACTIONS; HUMANS; LOGISTIC MODELS; MODELS, THEORETICAL; MOLECULAR STRUCTURE; PHARMACEUTICAL PREPARATIONS; PHARMACOKINETICS; ROC CURVE; SUPPORT VECTOR MACHINES;

EID: 84908145917     PISSN: None     EISSN: 1527974X     Source Type: Journal    
DOI: 10.1136/amiajnl-2013-002512     Document Type: Article

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