Predicting binding sites in the mouse genome

被引:0
作者
Sun, Yi [1 ]
Robinson, Mark [1 ]
Adams, Rod [1 ]
Davey, Neil [1 ]
Rust, Alistair [2 ]
机构
[1] Univ Hertfordshire, Sci & Technol Res Inst, Hatfield AL10 9AB, Herts, England
[2] Inst Syst Biol, Seattle, WA 98103 USA
来源
ICMLA 2007: SIXTH INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS, PROCEEDINGS | 2007年
关键词
D O I
10.1109/ICMLA.2007.28
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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 regulator), 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.
引用
收藏
页码:476 / +
页数:3
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