國家衛生研究院 NHRI:Item 3990099045/4053
English  |  正體中文  |  简体中文  |  全文笔数/总笔数 : 12145/12927 (94%)
造访人次 : 907768      在线人数 : 915
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
搜寻范围 查询小技巧:
  • 您可在西文检索词汇前后加上"双引号",以获取较精准的检索结果
  • 若欲以作者姓名搜寻,建议至进阶搜寻限定作者字段,可获得较完整数据
  • 进阶搜寻
    主页登入上传说明关于NHRI管理 到手机版


    jsp.display-item.identifier=請使用永久網址來引用或連結此文件: http://ir.nhri.org.tw/handle/3990099045/4053


    题名: A hub-attachment based method to detect functional modules from confidence-scored protein interactions and expression profiles
    作者: Chin, CH;Chen, SH;Ho, CW;Ko, MT;Lin, CY
    贡献者: Division of Biostatistics and Bioinformatics
    摘要: Background: Many research results show that the biological systems are composed of functional modules. Members in the same module usually have common functions. This is useful information to understand how biological systems work. Therefore, detecting functional modules is an important research topic in the post-genome era. One of functional module detecting methods is to find dense regions in Protein-Protein Interaction (PPI) networks. Most of current methods neglect confidence-scores of interactions, and pay little attention on using gene expression data to improve their results.Results: In this paper, we propose a novel hub-attachment based method to detect functional modules from confidence-scored protein interactions and expression profiles, and we name it HUNTER. Our method not only can extract functional modules from a weighted PPI network, but also use gene expression data as optional input to increase the quality of outcomes. Using HUNTER on yeast data, we found it can discover more novel components related with RNA polymerase complex than those existed methods from yeast interactome. And these new components show the close relationship with polymerase after functional analysis on Gene Ontology.Conclusion: A C++ implementation of our prediction method, dataset and supplementary material are available at http://hub.iis.sinica.edu.tw/Hunter/. Our proposed HUNTER method has been applied on yeast data, and the empirical results show that our method can accurately identify functional modules. Such useful application derived from our algorithm can reconstruct the biological machinery, identify undiscovered components and decipher common sub-modules inside these complexes like RNA polymerases I, II, III.
    日期: 2010-01-18
    關聯: BMC Bioinformatics. 2010 Jan 18;11(Suppl.1):Article number S25.
    Link to: http://dx.doi.org/10.1186/1471-2105-11-s1-s25
    JIF/Ranking 2023: http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=NHRI&SrcApp=NHRI_IR&KeyISSN=1471-2105&DestApp=IC2JCR
    Cited Times(WOS): https://www.webofscience.com/wos/woscc/full-record/WOS:000277537900006
    Cited Times(Scopus): http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=75149164017
    显示于类别:[林仲彥(1999-2005)] 期刊論文

    文件中的档案:

    档案 描述 大小格式浏览次数
    SCP75149164017.pdf3512KbAdobe PDF789检视/开启


    在NHRI中所有的数据项都受到原著作权保护.

    TAIR相关文章

    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - 回馈