Prediction of regulatory interactions in Arabidopsis using gene-expression data and support vector machines

Copyright © 2011 Elsevier Masson SAS. All rights reserved.

Bibliographische Detailangaben
Veröffentlicht in:Plant physiology and biochemistry : PPB. - 1991. - 49(2011), 3 vom: 15. März, Seite 280-3
1. Verfasser: Yu, Xiaoqing (VerfasserIn)
Weitere Verfasser: Liu, Taigang, Zheng, Xiaoqi, Yang, Zhongnan, Wang, Jun
Format: Online-Aufsatz
Sprache:English
Veröffentlicht: 2011
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
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245 1 0 |a Prediction of regulatory interactions in Arabidopsis using gene-expression data and support vector machines 
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520 |a 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 
650 4 |a Journal Article 
650 4 |a Research Support, Non-U.S. Gov't 
650 4 |a Validation Study 
650 7 |a Arabidopsis Proteins  |2 NLM 
650 7 |a Transcription Factors  |2 NLM 
700 1 |a Liu, Taigang  |e verfasserin  |4 aut 
700 1 |a Zheng, Xiaoqi  |e verfasserin  |4 aut 
700 1 |a Yang, Zhongnan  |e verfasserin  |4 aut 
700 1 |a Wang, Jun  |e verfasserin  |4 aut 
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