A bayesian network driven approach to model the transcriptional response to nitric oxide in Saccharomyces cerevisiae

PLoS ONE

Volumn 1, Issue 1, 2006, Pages

  Zhu, Jingchun ^a,c   Jambhekar, Ashwini ^a,b   Sraver, Aaron ^a,d   DeRisi, Joseph ^a,b  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

^c University of California Santa Cruz   (United States)

^d Ingenuity Systems Inc   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

GLUCOSE; HEMOGLOBIN; NITRIC OXIDE; DIOXYGENASE; HEMOPROTEIN; HYBRID PROTEIN; SACCHAROMYCES CEREVISIAE PROTEIN; YHB1 PROTEIN, S CEREVISIAE;

ALGORITHM; ARTICLE; BAYESIAN LEARNING; COMPUTER PROGRAM; CONTROLLED STUDY; ENVIRONMENT; EXPERIMENTATION; GENE EXPRESSION; GENETIC TRANSCRIPTION; SACCHAROMYCES CEREVISIAE; SIGNAL TRANSDUCTION; YEAST; ARTIFICIAL INTELLIGENCE; ARTIFICIAL NEURAL NETWORK; BAYES THEOREM; BIOLOGICAL MODEL; DNA MICROARRAY; DRUG EFFECT; FEEDBACK SYSTEM; FUNGAL GENE; GENE REGULATORY NETWORK; GENETICS; METABOLISM;

SACCHAROMYCES CEREVISIAE;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; BAYES THEOREM; DIOXYGENASES; FEEDBACK, PHYSIOLOGICAL; GENE REGULATORY NETWORKS; GENES, FUNGAL; GLUCOSE; HEMEPROTEINS; MODELS, BIOLOGICAL; NEURAL NETWORKS (COMPUTER); NITRIC OXIDE; OLIGONUCLEOTIDE ARRAY SEQUENCE ANALYSIS; RECOMBINANT FUSION PROTEINS; SACCHAROMYCES CEREVISIAE; SACCHAROMYCES CEREVISIAE PROTEINS; SOFTWARE; TRANSCRIPTION, GENETIC;

EID: 54949116534     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0000094     Document Type: Article

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