학술논문

Predicting Binding Sites in the Mouse Genome
Document Type
Conference
Source
Sixth International Conference on Machine Learning and Applications (ICMLA 2007) Machine Learning and Applications, 2007. ICMLA 2007. Sixth International Conference on. :476-481 Dec, 2007
Subject
Computing and Processing
Robotics and Control Systems
Mice
Genomics
Bioinformatics
Sequences
Machine learning algorithms
Machine learning
DNA
Support vector machines
Prediction algorithms
Fungi
Language
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.