An Integrative Approach to Infer Regulation Programs in a Transcription Regulatory Module Network.
The module network method, a special type of Bayesian network algorithms, has been proposed to infer transcription regulatory networks from gene expression data. In this method, a module represents a set of genes, which have similar expression profiles and are regulated by same transcription factors...
| Publicado en: | Journal of Biomedicine & Biotechnology Vol. 2012; pp. 1 - 9 |
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| Autores principales: | , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
2012
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=104298083&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104298083 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 11107243 137K jtl: Journal of Biomedicine & Biotechnology issn: 11107243 maglogo: N pubinfo: dt: 2012 vid: 2012 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 104298083 104298083 2011907004 NLM22577292 PMC3336162 104298083 ppf: 1 ppct: 8 formats: fmt: @attributes: type: P tig: atl: An Integrative Approach to Infer Regulation Programs in a Transcription Regulatory Module Network. aug: au: Jianlong Qi Michoel, Tom Butler, Gregory affil: Freiburg Institute for Advanced Studies, University of Freiburg, Albertstraße 19, 79104 Freiburg im Breisgau, Germany sug: subj: Algorithms Genetics Statistics and Numerical Data Gene Expression Descriptive Statistics Yeasts Validity Regression Models, Statistical ab: The module network method, a special type of Bayesian network algorithms, has been proposed to infer transcription regulatory networks from gene expression data. In this method, a module represents a set of genes, which have similar expression profiles and are regulated by same transcription factors. The process of learning module networks consists of two steps: first clustering genes into modules and then inferring the regulation program (transcription factors) of each module. Many algorithms have been designed to infer the regulation program of a given gene module, and these algorithms show very different biases in detecting regulatory relationships. In this work, we explore the possibility of integrating results from different algorithms. The integration methods we select are union, intersection, and weighted rank aggregation. Experiments in a yeast dataset show that the union and weighted rank aggregation methods produce more accurate predictions than those given by individual algorithms, whereas the intersection method does not yield any improvement in the accuracy of predictions. In addition, somewhat surprisingly, the union method, which has a lower computational cost than rank aggregation, achieves comparable results as given by rank aggregation. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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