Computational models for in-vitro anti-tubercular activity of molecules based on high-throughput chemical biology screening datasets

BMC Pharmacology

Volumn 12, Issue , 2012, Pages

  Periwal, Vinita ^a   Kishtapuram, Shireesha ^b   Scaria, Vinod ^a  

^a CSIR INSTITUTE OF GENOMICS AND INTEGRATIVE BIOLOGY   (India)

^b NATIONAL CHEMICAL LABORATORY   (India)

Author keywords

[No Author keywords available]

Indexed keywords

TUBERCULOSTATIC AGENT;

ARTICLE; BAYESIAN LEARNING; COMPUTER MODEL; DATA BASE; DRUG ACTIVITY; DRUG RESEARCH; HIGH THROUGHPUT SCREENING; MACHINE LEARNING; MATHEMATICAL MODEL; RANDOM FOREST; ALGORITHM; ARTIFICIAL INTELLIGENCE; BAYES THEOREM; CHEMISTRY; COMPUTER PROGRAM; COMPUTER SIMULATION; DRUG DEVELOPMENT; DRUG EFFECT; GENETIC DATABASE; HUMAN; LABORATORY DIAGNOSIS; MICROBIOLOGY; MULTIDRUG RESISTANT TUBERCULOSIS; MYCOBACTERIUM TUBERCULOSIS; PREDICTIVE VALUE; THEORETICAL MODEL;

ALGORITHMS; ANTITUBERCULAR AGENTS; ARTIFICIAL INTELLIGENCE; BAYES THEOREM; COMPUTER SIMULATION; DATABASES, GENETIC; DRUG DISCOVERY; FALSE NEGATIVE REACTIONS; FALSE POSITIVE REACTIONS; HIGH-THROUGHPUT SCREENING ASSAYS; HUMANS; MODELS, THEORETICAL; MYCOBACTERIUM TUBERCULOSIS; PREDICTIVE VALUE OF TESTS; SOFTWARE; TUBERCULOSIS, MULTIDRUG-RESISTANT;

EID: 84860490696     PISSN: None     EISSN: 14712210     Source Type: Journal    
DOI: 10.1186/1471-2210-12-1     Document Type: Article

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