2008 Special Issue: Combining experts in order to identify binding sites in yeast and mouse genomic data

Authors:
Mark Robinson;Cristina González Castellano;Faisal Rezwan;Rod Adams;Neil Davey;Alastair Rust;Yi Sun
Affiliations:
Science and Technology Research Institute, University of Hertfordshire, UK and Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, USA;Science and Technology Research Institute, University of Hertfordshire, UK;Science and Technology Research Institute, University of Hertfordshire, UK;Science and Technology Research Institute, University of Hertfordshire, UK;Science and Technology Research Institute, University of Hertfordshire, UK;Institute for Systems Biology, Seattle, USA;Science and Technology Research Institute, University of Hertfordshire, UK
Venue:
Neural Networks
Year:
2008

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Abstract

The identification of cis-regulatory binding sites in DNA is a difficult problem in computational biology. To obtain a full understanding of the complex machinery embodied in genetic regulatory networks it is necessary to know both the identity of the regulatory transcription factors and the location of their binding sites in the genome. We show that using an SVM together with data sampling to classify the combination of the results of individual algorithms specialised for the prediction of binding site locations, can produce significant improvements upon the original algorithms. The resulting classifier produces fewer false positive predictions and so reduces the expensive experimental procedure of verifying the predictions.