Prediction of regulatory interactions in Arabidopsis using gene-expression data and support vector machines
Copyright © 2011 Elsevier Masson SAS. All rights reserved.
| Veröffentlicht in: | Plant physiology and biochemistry : PPB. - 1991. - 49(2011), 3 vom: 15. März, Seite 280-3 |
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| 1. Verfasser: | |
| Weitere Verfasser: | , , , |
| Format: | Online-Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
2011
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| Zugriff auf das übergeordnete Werk: | Plant physiology and biochemistry : PPB |
| Schlagworte: | Journal Article Research Support, Non-U.S. Gov't Validation Study Arabidopsis Proteins Transcription Factors |
| Zusammenfassung: | Copyright © 2011 Elsevier Masson SAS. All rights reserved. Identification of regulatory relationships between transcription factors (TFs) and their targets is a central problem in post-genomic biology. In this paper, we apply an approach based on the support vector machine (SVM) and gene-expression data to predict the regulatory interactions in Arabidopsis. A set of 125 experimentally validated TF-target interactions and 750 negative regulatory gene pairs are collected as the training data. Their expression profiles data at 79 experimental conditions are fed to the SVM to perform the prediction. Through the jackknife cross-validation test, we find that the overall prediction accuracy of our approach achieves 88.68%. Our approach could help to widen the understanding of Arabidopsis gene regulatory scheme and may offer a cost-effective alternative to construct the gene regulatory network |
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| Beschreibung: | Date Completed 17.10.2011 Date Revised 30.09.2020 published: Print-Electronic Citation Status MEDLINE |
| ISSN: | 1873-2690 |
| DOI: | 10.1016/j.plaphy.2011.01.002 |