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Open Access Research article

Tandem machine learning for the identification of genes regulated by transcription factors

Deendayal Dinakarpandian1*, Venetia Raheja1, Saumil Mehta1, Erin G Schuetz2 and Peter K Rogan13

Author Affiliations

1 School of Computing and Engineering, University Of Missouri-Kansas City, Kansas City, Missouri, USA

2 Pharmaceutical Sciences, St. Jude's Children's Research Hospital, Memphis, Tennessee, USA

3 Laboratory of Human Molecular Genetics, Children's Mercy Hospital, Kansas City, Missouri, USA

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BMC Bioinformatics 2005, 6:204  doi:10.1186/1471-2105-6-204

Published: 22 August 2005

Abstract

Background

The identification of promoter regions that are regulated by a given transcription factor has traditionally relied upon the identification and distributions of binding sites recognized by the factor. In this study, we have developed a tandem machine learning approach for the identification of regulatory target genes based on these parameters and on the corresponding binding site information contents that measure the affinities of the factor for these cognate elements.

Results

This method has been validated using models of DNA binding sites recognized by the xenobiotic-sensitive nuclear receptor, PXR/RXRα, for target genes within the human genome. An information theory-based weight matrix was first derived and refined from known PXR/RXRα binding sites. The promoter region of candidate genes was scanned with the weight matrix. A novel information density-based clustering algorithm was then used to identify clusters of information rich sites. Finally, transformed data representing metrics of location, strength and clustering of binding sites were used for classification of promoter regions using an ensemble approach involving neural networks, decision trees and Naïve Bayesian classification. The method was evaluated on a set of 24 known target genes and 288 genes known not to be regulated by PXR/RXRα. We report an average accuracy (proportion of correctly classified promoter regions) of 71%, sensitivity of 73%, and specificity of 70%, based on multiple cross-validation and the leave-one-out strategy. The performance on a test set of 13 genes showed that 10 were correctly classified.

Conclusion

We have developed a machine learning approach for the successful detection of gene targets for transcription factors with high accuracy. The method has been validated for the transcription factor PXR/RXRα and has the potential to be extended to other transcription factors.