title: Combining experts in order to identify binding sites in yeast and mouse genomic data.
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.
authors: Science and Technology Research Institute, University of Hertfordshire, UK. M.Robinson@herts.ac.uk
authors: Mark
authors: M
authors: Robinson
authors: Cristina
authors: C
authors: González Castellano
authors: Faisal
authors: F
authors: Rezwan
authors: Rod
authors: R
authors: Adams
authors: Neil
authors: N
authors: Davey
authors: Alastair
authors: A
authors: Rust
authors: Yi
authors: Y
authors: Sun
mesh_terms: D000465:Algorithms; D000818:Animals; D001665:Binding Sites / Q000235:genetics; D019295:Computational Biology; D030541:Databases, Genetic / Q000706:statistics & numerical data; D015966:Gene Expression Regulation, Fungal; D016678:Genome / Q000235:genetics*; D023281:Genomics / Q000379:methods; D051379:Mice / Q000235:genetics* / Q000378:metabolism; D008957:Models, Genetic; D008969:Molecular Sequence Data; D011237:Predictive Value of Tests; D014157:Transcription Factors / Q000737:chemistry / Q000378:metabolism*; D015003:Yeasts / Q000235:genetics / Q000378:metabolism*
chemical_list: D014157:Transcription Factors