Combining experts in order to identify binding sites in yeast and mouse genomic data

Mark Robinson, Cristina González Castellano, F. Rezwan, Rod Adams, Neil Davey, Alastair Rust, Yi Sun

Research output: Contribution to journalArticlepeer-review

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.
Original languageEnglish
Pages (from-to)856-861
Number of pages6
JournalNeural Networks
Volume21
Issue number6
DOIs
Publication statusPublished - 21 Aug 2008

Keywords

  • Computational biology
  • Imbalanced data
  • Sampling
  • Support vector machine
  • Transcription factor binding sites

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