Improved prediction of transcription binding sites from chromatin modification data

Kengo Sato, Tom Whitington, Timothy L. Bailey, Paul Horton

研究成果: Conference contribution

摘要

In this paper we apply machine learning to the task of predicting transcription factor binding sites by combining information on multiple forms of chromatin modification with the binding strength DNA site predicted by a position weight matrix. We additionally explore the effect of incorporating auxiliary features such as the distance of the site to the nearest gene's transcription start site and the degree to which the site is conserved among related species. We approach the task as a classification problem, and show that both Naïve Bayes and Random Forests can provide substantial increases in the accuracy of predicted binding sites. Our results extend previous work which simply filtered candidate sites based on H3K4Me3 chromatin modification scores. In addition we apply feature selection to explore which forms of chromatin modification and which auxiliary features have predictive value for which transcription factors.

原文English
主出版物標題2010 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2010
頁面220-226
頁數7
DOIs
出版狀態Published - 2010 8月 20
事件2010 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2010 - Montreal, QC, Canada
持續時間: 2010 5月 22010 5月 5

出版系列

名字2010 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2010

Other

Other2010 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2010
國家/地區Canada
城市Montreal, QC
期間10-05-0210-05-05

All Science Journal Classification (ASJC) codes

  • 人工智慧
  • 計算機理論與數學
  • 生物醫學工程

指紋

深入研究「Improved prediction of transcription binding sites from chromatin modification data」主題。共同形成了獨特的指紋。

引用此