Predicting drug target interactions using meta-path-based semantic network analysis

BMC Bioinformatics

Volumn 17, Issue 1, 2016, Pages

  Fu, Gang ^a   Ding, Ying ^b,c   Seal, Abhik ^b   Chen, Bin ^d   Sun, Yizhou ^e   Bolton, Evan ^a  

^a NATIONAL INSTITUTES OF HEALTH   (United States)

^b INDIANA UNIVERSITY   (United States)

^c WUHAN UNIVERSITY   (China)

^d STANFORD UNIVERSITY SCHOOL OF MEDICINE   (United States)

^e Northeastern University   (United States)

Author keywords

Link prediction; Machine learning; Meta path topological feature; Random forest; Semantic network analysis

Indexed keywords

ARTIFICIAL INTELLIGENCE; DECISION TREES; DRUG INTERACTIONS; FEATURE EXTRACTION; LEARNING SYSTEMS; PROTEINS; SEMANTIC WEB; SEMANTICS; SUPERVISED LEARNING; TOPOLOGY;

CHEMICAL AND BIOLOGICALS; DRUG-TARGET INTERACTIONS; LINK PREDICTION; RANDOM FOREST ALGORITHM; RANDOM FORESTS; SEMANTIC NETWORK ANALYSIS; SUPERVISED MACHINE LEARNING; TOPOLOGICAL FEATURES;

FORECASTING;

DRUG; PROTEIN;

CHEMICAL MODEL; CHEMISTRY; DATA MINING; DRUG DESIGN; FACTUAL DATABASE; MACHINE LEARNING; PHARMACOLOGY;

DATA MINING; DATABASES, FACTUAL; DRUG DESIGN; MACHINE LEARNING; MODELS, CHEMICAL; PHARMACEUTICAL PREPARATIONS; PHARMACOLOGICAL PHENOMENA; PROTEINS;

EID: 85007471039     PISSN: None     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/s12859-016-1005-x     Document Type: Article

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