Predicting Binding Sites in the Mouse Genome

Yi Sun, M. Robinson, R. Adams, N. Davey, A. Rust
{"title":"Predicting Binding Sites in the Mouse Genome","authors":"Yi Sun, M. Robinson, R. Adams, N. Davey, A. Rust","doi":"10.1109/ICMLA.2007.28","DOIUrl":null,"url":null,"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.","PeriodicalId":448863,"journal":{"name":"Sixth International Conference on Machine Learning and Applications (ICMLA 2007)","volume":"104 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2007-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Sixth International Conference on Machine Learning and Applications (ICMLA 2007)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICMLA.2007.28","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3

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.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
预测小鼠基因组中的结合位点
多细胞真核生物DNA顺式调控结合位点的鉴定是计算生物学中一个特别困难的问题。为了充分了解遗传调控网络中的复杂机制,有必要了解调控转录因子的身份及其在基因组中结合位点的位置。我们表明,将支持向量机与数据采样一起使用,整合专门用于预测结合位点位置的单个算法的结果,可以对应用于小鼠基因组的原始算法产生显着改进。这些结果使实际验证预测的昂贵实验过程更加容易处理。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
SVMotif: A Machine Learning Motif Algorithm A Statistical Algorithm to Discover Knowledge in Medical Data Sources A New Ant Evolution Algorithm to Resolve TSP Problem Tracking recurrent concept drift in streaming data using ensemble classifiers Model evaluation for prognostics: estimating cost saving for the end users
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1