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Prediction of regulatory interactions in Arabidopsis using gene-expression data and support vector machines
- Source :
- Plant Physiology and Biochemistry. 49:280-283
- Publication Year :
- 2011
- Publisher :
- Elsevier BV, 2011.
-
Abstract
- 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.
- Subjects :
- Physiology
Arabidopsis
Gene regulatory network
Gene Expression
Plant Science
Computational biology
Genes, Plant
Bioinformatics
Artificial Intelligence
Gene Expression Regulation, Plant
Genetics
Gene Regulatory Networks
Gene
Regulator gene
biology
Arabidopsis Proteins
Gene Expression Profiling
Computational Biology
Reproducibility of Results
biology.organism_classification
Expression (mathematics)
Support vector machine
Identification (information)
Jackknife resampling
Transcription Factors
Subjects
Details
- ISSN :
- 09819428
- Volume :
- 49
- Database :
- OpenAIRE
- Journal :
- Plant Physiology and Biochemistry
- Accession number :
- edsair.doi.dedup.....cd16cc3cf87d60ca7a0138cd51317f82