| Uncovering gene regulatory networks from time-series microarray data with variational Bayesian structural expectation maximization. | |
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PMID: 18309364 Owner: NLM Status: PubMed-not-MEDLINE |
Abstract/OtherAbstract:
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We investigate in this paper reverse engineering of gene regulatory networks from time-series microarray data. We apply dynamic Bayesian networks (DBNs) for modeling cell cycle regulations. In developing a network inference algorithm, we focus on soft solutions that can provide a posteriori probability (APP) of network topology. In particular, we propose a variational Bayesian structural expectation maximization algorithm that can learn the posterior distribution of the network model parameters and topology jointly. We also show how the obtained APPs of the network topology can be used in a Bayesian data integration strategy to integrate two different microarray data sets. The proposed VBSEM algorithm has been tested on yeast cell cycle data sets. To evaluate the confidence of the inferred networks, we apply a moving block bootstrap method. The inferred network is validated by comparing it to the KEGG pathway map. |
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Authors:
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Isabel Tienda Luna; Yufei Huang; Yufang Yin; Diego P Ruiz Padillo; M Carmen Carrion Perez |
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Publication Detail:
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Type: Journal Article |
Journal Detail:
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Title: EURASIP journal on bioinformatics & systems biology Volume: - ISSN: 1687-4145 ISO Abbreviation: EURASIP J Bioinform Syst Biol Publication Date: 2007 |
Date Detail:
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Created Date: 2008-02-29 Completed Date: 2010-06-28 Revised Date: 2011-09-15 |
Medline Journal Info:
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Nlm Unique ID: 101263720 Medline TA: EURASIP J Bioinform Syst Biol Country: United States |
Other Details:
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Languages: eng Pagination: 71312 Citation Subset: - |
Affiliation:
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Department of Applied Physics, University of Granada, Granada, Spain. |
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ID/Acronym/Agency:
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R21 AI067543-01A1/AI/NIAID NIH HHS |
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From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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Journal Information Journal ID (nlm-ta): EURASIP J Bioinform Syst Biol ISSN: 1687-4145 ISSN: 1687-4153 Publisher: BioMed Central |
Article Information Download PDF  Copyright ©2007 Isabel Tienda Luna et al. open-access: Received Day: 1 Month: 7 Year: 2006 Revision Received Day: 4 Month: 12 Year: 2006 Accepted Day: 11 Month: 5 Year: 2007 Print publication date: Year: 2007 Electronic publication date: Day: 27 Month: 6 Year: 2007 Volume: 2007 Issue: 1 First Page: 71312 Last Page: 71312 ID: 3171349 Publisher Id: 1687-4153-2007-71312 PubMed Id: 18309364 DOI: 10.1155/2007/71312 |
| Uncovering Gene Regulatory Networks from Time-Series Microarray Data with Variational Bayesian Structural Expectation Maximization | |
| Isabel Tienda Luna1 | Email: isabelt@ugr.es |
| Yufei Huang2 | Email: yufei.huang@utsa.edu |
| Yufang Yin2 | Email: yyin@lonestar.utsa.edu |
| Diego P Ruiz Padillo1 | Email: druiz@ugr.es |
| M Carmen Carrion Perez1 | Email: mcarrion@ugr.es |
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1Department of Applied Physics, University of Granada, Granada 18071, Spain |
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2Department of Electrical and Computer Engineering, University of Texas at San Antonio (UTSA), San Antonio, TX 78249-0669, USA |
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References
| Kitano H,Looking beyond that details: a rise in system-oriented approaches in genetics and molecular biologyCurrent GeneticsYear: 200241111010.1007/s00294-002-0285-z12073094 | |
| D'haeseleer P,Liang S,Somogyi R,Genetic network inference: from co-expression clustering to reverse engineeringBioinformaticsYear: 200016870772610.1093/bioinformatics/16.8.70711099257 | |
| Brazhnik P,de la Fuente A,Mendes P,Gene networks: how to put the function in genomicsTrends in BiotechnologyYear: 2002201146747210.1016/S0167-7799(02)02053-X12413821 | |
| Friedman N,Inferring cellular networks using probabilistic graphical modelsScienceYear: 2004303565979980510.1126/science.109406814764868 | |
| Shmulevich I,Dougherty ER,Kim S,Zhang W,Probabilistic Boolean networks: a rule-based uncertainty model for gene regulatory networksBioinformaticsYear: 200218226127410.1093/bioinformatics/18.2.26111847074 | |
| Zhou X,Wang X,Dougherty ER,Construction of genomic networks using mutual-information clustering and reversible-jump Markov-chain-Monte-Carlo predictor designSignal ProcessingYear: 200383474576110.1016/S0165-1684(02)00469-3 | |
| Hartemink AJ,Gifford DK,Jaakkola TS,Young RA,Using graphical models and genomic expression data to statistically validate models of genetic regulatory networksProceedings of the 6th Pacific Symposium on Biocomputing (PSB '01), The Big Island of Hawaii, Hawaii, USA, January 2001422433 | |
| Moler EJ,Radisky DC,Mian IS,Integrating naive Bayes models and external knowledge to examine copper and iron homeostasis in S. cerevisiaePhysiol GenomicsYear: 20004212713511120873 | |
| Segal E,Rich probabilistic models for genomic data, Ph.D. thesisYear: 2004Stanford University, Stanford, Calif, USA | |
| de Jong H,Modeling and simulation of genetic regulatory systems: a literature reviewJournal of Computational BiologyYear: 2002916710310.1089/1066527025283320811911796 | |
| Bar-Joseph Z,Analyzing time series gene expression dataBioinformaticsYear: 200420162493250310.1093/bioinformatics/bth28315130923 | |
| Simonis N,Wodak SJ,Cohen GN,van Helden J,Combining pattern discovery and discriminant analysis to predict gene co-regulationBioinformaticsYear: 200420152370237910.1093/bioinformatics/bth25215073004 | |
| Murphy K,Mian S,Modelling gene expression data using dynamic Bayesian networksYear: 1999Computer Science Division, University of California, Berkeley, Calif, USA | |
| Friedman N,Linial M,Nachman I,Pe'er D,Using Bayesian networks to analyze expression dataJournal of Computational BiologyYear: 200073-460162010.1089/10665270075005096111108481 | |
| van Berlo RJP,van Someren EP,Reinders MJT,Studying the conditions for learning dynamic Bayesian networks to discover genetic regulatory networksSimulationYear: 20037912689702 | |
| Beal MJ,Falciani F,Ghahramani Z,Rangel C,Wild DL,A Bayesian approach to reconstructing genetic regulatory networks with hidden factorsBioinformaticsYear: 200521334935610.1093/bioinformatics/bti01415353451 | |
| Perrin B-E,Ralaivola L,Mazurie A,Bottani S,Mallet J,d'Alché-Buc F,Gene networks inference using dynamic Bayesian networksBioinformaticsYear: 2003192ii138ii14810.1093/bioinformatics/btg107114534183 | |
| Kim SY,Imoto S,Miyano S,Inferring gene networks from time series microarray data using dynamic Bayesian networksBriefings in BioinformaticsYear: 20034322823510.1093/bib/4.3.22814582517 | |
| Ferrazzi F,Amici R,Sebastiani P,Kohane IS,Ramoni MF,Bellazzi R,Can we use linear Gaussian networks to model dynamic interactions among genes? Results from a simulation studyProceedings of IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS '06), College Station, Tex, USA, May 20061314 | |
| Wang X,Poor HV,Wireless Communication Systems: Advanced Techniques for Signal ReceptionYear: 2004Prentice Hall PTR, Englewood Cliffs, NJ, USA | |
| Wang J,Huang Y,Sanchez M,Wang Y,Zhang J,Reverse engineering yeast gene regulatory networks using graphical modelsProceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '06), Toulouse, France, May 2006210881091 | |
| Husmeier D,Sensitivity and specificity of inferring genetic regulatory interactions from microarray experiments with dynamic Bayesian networksBioinformaticsYear: 200319172271228210.1093/bioinformatics/btg31314630656 | |
| Murphy KP,Dynamic Bayesian networks: representation, inference and learning, Ph.D. thesisYear: 2004University of California, Berkeley, Calif, USA | |
| Kay SM,Fundamentals of Statistical Signal Processing: Estimation TheoryYear: 1997Prentice-Hall, Englewood Cliffs, NJ, USA | |
| Beal MJ,Variational algorithms for approximate Bayesian inference, Ph.D. thesisYear: 2003The Gatsby Computational Neuroscience Unit, University College London, London, UK | |
| Brooks SP,Markov chain Monte Carlo method and its applicationJournal of the Royal Statistical Society: Series D, The StatisticianYear: 19984716910010.1111/1467-9884.00117 | |
| Spellman PT,Sherlock G,Zhang MQ,et al. Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridizationMolecular Biology of the CellYear: 1998912327332979843569 | |
| Cho RJ,Campbell MJ,Winzeler EA,et al. A genome-wide transcriptional analysis of the mitotic cell cycleMolecular CellYear: 199821657310.1016/S1097-2765(00)80114-89702192 | |
| Efron B,Tibshirani R,An Introduction to Bootstrap, Monographs on Statistics and Applied Probability, no. 57Year: 1993Chapman & Hall, New York, NY, USA | |
| Lahiri SN,Resampling Methods for Dependent DataYear: 2003Springer, New York, NY, USA | |
| Kegg: Kyoto encyclopedia of genes and genomes http://www.genome.jp/kegg/. |
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