Extraction of Genetic Networks (GN) Using Static Bayesian Belief Networks From Genome-Wide Temporal Microarray Data

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Extraction of Genetic Networks (GN) Using Static Bayesian Belief Networks From Genome-Wide Temporal Microarray Data
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  BioMed   Central Page 1 of 1 (page number not for citation purposes) BMC Bioinformatics Open Access Oral presentation Extraction of Genetic Networks (GN) Using Static Bayesian Belief Networks From Genome-Wide Temporal Microarray Data JuditKumuthini*  Address: Cranfield University, Department of Analytical Science and Informatics (DASI) Cranfield University, Barton Rd, Silsoe, Bedfordshire, MK45 4DT, UK.Email: JuditKumuthini*-S.j.kumuthini.s02@cranfield.ac.uk * Corresponding author  The extraction of Gene Regulatory Network (GRN) for genome-wide microarray data is still at an early stage. Inthis project Bayesian belief networks (BBN), a graph basedrepresentation of joint probability distributions that cap-ture the conditional dependencies between genes areapplied to genome-wide E coli microarray data. Theextracted GRN is found to be valid when compared withgenome pathway databases such as KEGG and Ecocyc. Inaddition, a novel quantitative simulation technique wasapplied to the extracted BN to learn the marginal proba-bilities expression status of all the genes in the network. We present GRNs, extracted using the Taboo and tabooorder algorithms from temporal expression data for 4000genes. Missing values were inferred using the EM algo-rithm. The extracted GRN shows strong similarity with theactual classification of the phases of yeast cell cycle. Thearchitecture of individual pathway and inter pathway rela-tion ship among these genes were largely preserved. Fur-ther, we also present the effect of different discritizationclass width of normalised Log2 expression ratios and theimpact on the GRN architecture. This GRN provides a richer model for analysing geneexpression patterns, which captures the interactionamong various genes in terms of probabilities and condi-tional dependencies. The results of this proof of concept study demonstrate that the BBN approach is well suited tomodelling DNA microarray data, which is characterisedby a high degree of measurement noise and variability. These results, and BBN's strengths in stochastic modelling and incorporation of prior knowledge, indicate potentialfor determining previously unknown or incompleteGRNs. This work was supervised by Dr Conrad Bessant,Prof. Selly Saini. from BioSysBio: Bioinformatics and Systems Biology ConferenceEdinburgh, UK, 14–15 July 2005Published: 21 September 2005 BMC Bioinformatics  2005, 6 (Suppl 3):S8 <supplement> <title><p>BioSysBio:Bioinformatics and Systems BiologyConference</p></title> <note>Meetingabstracts – Asingle PDF containingallabstracts in this Supplement is available <ahref="http://www.biomedcentral.com/content/files/pdf/1471-2105-6-S3-full.pdf">here</a>.</note><url>http://www.biomedcentral.com/content/pdf/1471-2105-6-S3-info.pdf</url> </supplement>
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