A Bayesian approach to joint modeling of protein-DNA binding, gene expression and sequence data

Statistics in Medicine

Volumn 29, Issue 4, 2010, Pages 489-503

  Xie, Yang ^a,b   Pan, Wei ^c   Jeong, Kyeong S ^d   Xiao, Guanghua ^a   Khodursky, Arkady B ^e  

^a UNIVERSITY OF TEXAS SOUTHWESTERN MEDICAL CENTER   (United States)

^b UNIVERSITY OF TEXAS SOUTHWESTERN MEDICAL CENTER   (United States)

^c UNIVERSITY OF MINNESOTA   (United States)

^d UNIVERSITY OF CALIFORNIA   (United States)

^e UNIVERSITY OF MINNESOTA   (United States)

Author keywords

Bayesian model; ChIP chip data; Joint modeling; Microarray

Indexed keywords

ARTICLE; BAYES THEOREM; DNA SEQUENCE; GENE EXPRESSION; GENE TARGETING; GENOME; INFORMATION PROCESSING; PROTEIN DNA BINDING; RECEIVER OPERATING CHARACTERISTIC; SIMULATION; STATISTICAL MODEL;

BAYES THEOREM; COMPUTER SIMULATION; DNA; ESCHERICHIA COLI; GENE EXPRESSION; GENE EXPRESSION REGULATION, BACTERIAL; LEUCINE-RESPONSIVE REGULATORY PROTEIN; MARKOV CHAINS; MODELS, STATISTICAL; MONTE CARLO METHOD; PROTEIN BINDING; REGULON; SEQUENCE ANALYSIS, DNA;

EID: 76549101232     PISSN: 02776715     EISSN: 10970258     Source Type: Journal    
DOI: 10.1002/sim.3815     Document Type: Article

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