Title :
Predicting Binding Sites in the Mouse Genome
Author :
Sun, Yi ; Robinson, Mark ; Adams, Rod ; Davey, Neil ; Rust, Alistair
Author_Institution :
Univ. of Hertfordshire, Hatfield
Abstract :
The identification of cis-regulatory binding sites in DNA in multicellular eukaryotes is a particularly 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 together with the location of their binding sites in the genome. We show that using an SVM together with data sampling, to integrate the results of individual algorithms specialised for the prediction of binding site locations, can produce significant improvements upon the original algorithms applied to the mouse genome. These results make more tractable the expensive experimental procedure of actually verifying the predictions.
Keywords :
DNA; biology computing; genetics; support vector machines; DNA; cis-regulatory binding site; computational biology; genetic regulatory network; mouse genome; multicellular eukaryotes; support vector machine; Bioinformatics; DNA; Fungi; Genomics; Machine learning; Machine learning algorithms; Mice; Prediction algorithms; Sequences; Support vector machines;
Conference_Titel :
Machine Learning and Applications, 2007. ICMLA 2007. Sixth International Conference on
Conference_Location :
Cincinnati, OH
Print_ISBN :
978-0-7695-3069-7
DOI :
10.1109/ICMLA.2007.28