Comparison of reversible-jump Markov-chain-Monte-Carlo learning approach with other methods for missing enzyme identification

Journal of Biomedical Informatics

Volumn 41, Issue 2, 2008, Pages 272-281

  Geng, Bo ^a,b   Zhou, Xiaobo ^b   Zhu, Jinmin ^b   Hung, Y S ^a   Wong, Stephen T C ^b,c  

^a UNIVERSITY OF HONG KONG   (Hong Kong)

^b HARVARD MEDICAL SCHOOL   (United States)

^c BRIGHAM AND WOMEN S HOSPITAL   (United States)

Author keywords

Markov chain Monte Carlo; Metabolic network; Missing enzymes identification; Regression model

Indexed keywords

BAYESIAN NETWORKS; COMPUTATIONAL MECHANICS; ENZYME SENSORS; MARKOV PROCESSES; MONTE CARLO METHODS; PROBLEM SOLVING; REGRESSION ANALYSIS;

MARKOV CHAIN MONTE CARLO; METABOLIC NETWORKS; MISSING ENZYMES IDENTIFICATION; REGRESSION MODELS;

LEARNING SYSTEMS;

ARTICLE; ARTIFICIAL NEURAL NETWORK; BAYES THEOREM; CAULOBACTER CRESCENTUS; ENZYME ANALYSIS; ESCHERICHIA COLI; MATHEMATICAL COMPUTING; MONTE CARLO METHOD; MYCOBACTERIUM TUBERCULOSIS; PRIORITY JOURNAL; PROBABILITY; REGRESSION ANALYSIS; SACCHAROMYCES CEREVISIAE; SENSITIVITY AND SPECIFICITY; STATISTICAL MODEL; SUPPORT VECTOR MACHINE; VIBRIO CHOLERAE;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; COMPUTER SIMULATION; GENE EXPRESSION PROFILING; MODELS, BIOLOGICAL; MODELS, STATISTICAL; MONTE CARLO METHOD; MULTIENZYME COMPLEXES; PATTERN RECOGNITION, AUTOMATED; SIGNAL TRANSDUCTION;

EID: 40049107547     PISSN: 15320464     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jbi.2007.09.002     Document Type: Article

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