Learning causal networks from systems biology time course data: An effective model selection procedure for the vector autoregressive process

BMC Bioinformatics

Volumn 8, Issue SUPPL. 2, 2007, Pages

  Opgen Rhein, Rainer ^a   Strimmer, Korbinian ^b  

^a UNIVERSITY OF MUNICH   (Germany)

^b UNIVERSITY OF LEIPZIG   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

ARABIDOPSIS THALIANA; AUTO REGRESSIVE PROCESS; HIGH DIMENSIONALITY; MODEL SELECTION PROCEDURES; PARTIAL CORRELATION; REGRESSION COEFFICIENT; REGULATORY INTERACTIONS; STATISTICAL LEARNING;

MOLECULAR BIOLOGY; PLANTS (BOTANY); STATISTICAL MECHANICS;

VALUE ENGINEERING;

ARABIDOPSIS; ARTICLE; CAUSAL MODELING; COMPUTER NETWORK; GENE EXPRESSION; GENOMICS; NONHUMAN; PLANT GENETICS; PROTEOMICS; SAMPLE SIZE; STATISTICAL MODEL; TIME SERIES ANALYSIS; VECTOR AUTOREGRESSIVE PROCESS MODEL; ALGORITHM; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; BIOLOGICAL MODEL; COMPUTER SIMULATION; EVALUATION; GENE EXPRESSION REGULATION; METABOLISM; METHODOLOGY; PHYSIOLOGY; REGRESSION ANALYSIS; SIGNAL TRANSDUCTION; SYSTEMS BIOLOGY; TIME;

ARABIDOPSIS THALIANA;

PROTEOME;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; COMPUTER SIMULATION; GENE EXPRESSION REGULATION; MODELS, BIOLOGICAL; PATTERN RECOGNITION, AUTOMATED; PROTEOME; REGRESSION ANALYSIS; SIGNAL TRANSDUCTION; SYSTEMS BIOLOGY; TIME FACTORS;

EID: 34249862287     PISSN: None     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/1471-2105-8-S2-S3     Document Type: Article

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