Gaussian graphical model for identifying significantly responsive regulatory networks from time course high-throughput data

IET Systems Biology

Volumn 7, Issue 5, 2013, Pages 143-152

  Liu, Zhi Ping ^a   Zhang, Wanwei ^b   Horimoto, Katsuhisa ^c   Chen, Luonan ^b  

^a SHANDONG UNIVERSITY   (China)

^b Chinese Academy of Sciences   (China)

^c NATIONAL INSTITUTE OF ADVANCED INDUSTRIAL SCIENCE AND TECHNOLOGY AIST   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

COMPREHENSIVE INFORMATION; CONSISTENCY MEASUREMENT; DYNAMIC BAYESIAN NETWORKS; FUNCTIONAL RELATIONSHIP; GAUSSIAN GRAPHICAL MODELS; GENE EXPRESSION PROFILING; GENE REGULATORY NETWORKS; TIME-COURSE GENE EXPRESSION PROFILES;

GRAPHIC METHODS; KNOWLEDGE BASED SYSTEMS; NETWORK ARCHITECTURE;

GENE EXPRESSION;

ADIPOSE TISSUE; ALGORITHM; ANIMAL; BIOLOGY; CIRCADIAN RHYTHM; COMPUTER GRAPHICS; COMPUTER SIMULATION; DNA MICROARRAY; GENE EXPRESSION PROFILING; GENE REGULATORY NETWORK; HIGH THROUGHPUT SEQUENCING; LUNG; METABOLISM; NORMAL DISTRIBUTION; PHENOTYPE; PROCEDURES; RAT; STATISTICAL MODEL; SYSTEMS BIOLOGY; TIME; WISTAR RAT; ARTICLE; METHODOLOGY;

ADIPOSE TISSUE; ALGORITHMS; ANIMALS; CIRCADIAN RHYTHM; COMPUTATIONAL BIOLOGY; COMPUTER GRAPHICS; COMPUTER SIMULATION; GENE EXPRESSION PROFILING; GENE REGULATORY NETWORKS; HIGH-THROUGHPUT NUCLEOTIDE SEQUENCING; LUNG; MODELS, STATISTICAL; NORMAL DISTRIBUTION; OLIGONUCLEOTIDE ARRAY SEQUENCE ANALYSIS; PHENOTYPE; RATS; RATS, WISTAR; SYSTEMS BIOLOGY; TIME FACTORS;

EID: 84893156375     PISSN: 17518849     EISSN: None     Source Type: Journal    
DOI: 10.1049/iet-syb.2012.0062     Document Type: Article

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