Fusing dual-event data sets for mycobacterium tuberculosis machine learning models and their evaluation

Journal of Chemical Information and Modeling

Volumn 53, Issue 11, 2013, Pages 3054-3063

  Ekins, Sean ^a,b   Freundlich, Joel S ^c   Reynolds, Robert C ^d  

^a Collaborative Drug Discovery ^*  (United States)

^b Collaborations in Chemistry ^*  (United States)

^c RUTGERS NEW JERSEY MEDICAL SCHOOL   (United States)

^d University of Alabama at Birmingham   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CYTOTOXICITY; DATA FUSION; MOLECULES; REGRESSION ANALYSIS; SUPPORT VECTOR MACHINES;

ANTI TUBERCULARS; ANTITUBERCULAR ACTIVITIES; HIGH-THROUGHPUT SCREENS; MACHINE LEARNING MODELS; MYCOBACTERIUM TUBERCULOSIS; RECEIVER OPERATOR CURVES; RECURSIVE PARTITIONING MODEL; SINGLE- MACHINES;

LEARNING SYSTEMS;

CYTOTOXIN; TUBERCULOSTATIC AGENT;

ANIMAL; ARTICLE; ARTIFICIAL INTELLIGENCE; BAYES THEOREM; CELL SURVIVAL; CHEMISTRY; CHLOROCEBUS AETHIOPS; COMPUTER INTERFACE; DECISION TREE; DRUG EFFECT; HUMAN; MYCOBACTERIUM TUBERCULOSIS; RECEIVER OPERATING CHARACTERISTIC; SUPPORT VECTOR MACHINE; VERO CELL LINE;

ANIMALS; ANTITUBERCULAR AGENTS; ARTIFICIAL INTELLIGENCE; BAYES THEOREM; CELL SURVIVAL; CERCOPITHECUS AETHIOPS; CYTOTOXINS; DECISION TREES; HUMANS; MYCOBACTERIUM TUBERCULOSIS; ROC CURVE; SUPPORT VECTOR MACHINES; USER-COMPUTER INTERFACE; VERO CELLS;

EID: 84888586878     PISSN: 15499596     EISSN: 1549960X     Source Type: Journal    
DOI: 10.1021/ci400480s     Document Type: Article

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