We have located links that may give you full text access.
JOURNAL ARTICLE
RESEARCH SUPPORT, NON-U.S. GOV'T
VALIDATION STUDIES
Prediction of regulatory interactions in Arabidopsis using gene-expression data and support vector machines.
Plant Physiology and Biochemistry : PPB 2011 March
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.
Full text links
Get seemless 1-tap access through your institution/university
For the best experience, use the Read mobile app
All material on this website is protected by copyright, Copyright © 1994-2024 by WebMD LLC.
This website also contains material copyrighted by 3rd parties.
By using this service, you agree to our terms of use and privacy policy.
Your Privacy Choices
You can now claim free CME credits for this literature searchClaim now
Get seemless 1-tap access through your institution/university
For the best experience, use the Read mobile app