Bayesian network and nonparametric heteroscedastic regression for nonlinear modeling of genetic network

Proceedings - IEEE Computer Society Bioinformatics Conference, CSB 2002

Volumn , Issue , 2002, Pages 219-227

  Imoto, S ^a   Sunyong, Kim ^a   Goto, T ^a   Aburatani, S ^b   Tashiro, K ^b   Kuhara, S ^b   Miyano, S ^a  

^a UNIVERSITY OF TOKYO   (Japan)

^b KYUSHU UNIVERSITY   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

BIOINFORMATICS; GENE EXPRESSION; GENES; KNOWLEDGE BASED SYSTEMS; REGRESSION ANALYSIS; YEAST;

CONDITIONAL DISTRIBUTION; GENE EXPRESSION DATA; HETEROSCEDASTIC REGRESSIONS; MICROARRAY GENE EXPRESSION DATA; NETWORK CONSTRUCTION; NON-PARAMETRIC REGRESSION; NONLINEAR STRUCTURE; SELECTION CRITERIA;

BAYESIAN NETWORKS;

SACCHAROMYCES CEREVISIAE PROTEIN; TRANSCRIPTION FACTOR;

ARTICLE; ARTIFICIAL INTELLIGENCE; BAYES THEOREM; BIOLOGICAL MODEL; DNA MICROARRAY; EVALUATION; GENE EXPRESSION PROFILING; GENE EXPRESSION REGULATION; GENETICS; METABOLISM; METHODOLOGY; NONLINEAR SYSTEM; PHYSIOLOGY; REGRESSION ANALYSIS; SIGNAL TRANSDUCTION; STATISTICAL MODEL; STATISTICS;

ARTIFICIAL INTELLIGENCE; BAYES THEOREM; GENE EXPRESSION PROFILING; GENE EXPRESSION REGULATION; MODELS, GENETIC; MODELS, STATISTICAL; NONLINEAR DYNAMICS; OLIGONUCLEOTIDE ARRAY SEQUENCE ANALYSIS; REGRESSION ANALYSIS; SACCHAROMYCES CEREVISIAE PROTEINS; SIGNAL TRANSDUCTION; STOCHASTIC PROCESSES; TRANSCRIPTION FACTORS;

EID: 0012969768     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CSB.2002.1039344     Document Type: Conference Paper

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