Using higher-order dynamic bayesian networks to model periodic data from the circadian clock of arabidopsis thaliana

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 5780 LNBI, Issue , 2009, Pages 67-78

  Daly, Rónán ^a   Edwards, Kieron D ^b   O'Neill, John S ^c   Aitken, Stuart ^d   Millar, Andrew J ^d   Girolami, Mark ^a  

^a UNIVERSITY OF GLASGOW   (United Kingdom)

^b Advanced Technologies Cambridge Limited   (United Kingdom)

^c UNIVERSITY OF CAMBRIDGE   (United Kingdom)

^d UNIVERSITY OF EDINBURGH   (United Kingdom)

Author keywords

Arabidopsis Thaliana; Dynamic Bayesian Network; Gene Expression; Gene Regulatory Network

Indexed keywords

ARABIDOPSIS THALIANA; CIRCADIAN CLOCK; DYNAMIC BAYESIAN NETWORK; GENE REGULATORY NETWORK; GENE REGULATORY NETWORKS; HIGHER-ORDER DYNAMICS; TIME LAG;

BIOACTIVITY; BIOINFORMATICS; DISTRIBUTED PARAMETER NETWORKS; GENE EXPRESSION; INFERENCE ENGINES; INTELLIGENT NETWORKS; SPEECH RECOGNITION;

BAYESIAN NETWORKS;

EID: 70349858154     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-04031-3_7     Document Type: Conference Paper

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