A hub-attachment based method to detect functional modules from confidence-scored protein interactions and expression profiles

doi:10.1186/1471-2105-11-S1-S25

南華大學機構典藏系統 > 科技學院 > 資訊工程學系 > 期刊論文 > Item 987654321/26812

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题名:	A hub-attachment based method to detect functional modules from confidence-scored protein interactions and expression profiles
作者:	金家豪;Chin, Chia-Hao;Chen, Shu-Hwa;Ho, Chin-Wen;Ko, Ming-Tat;Lin, Chung-Yen
貢獻者:	資訊工程學系
关键词:	Gene Ontology;Gene Expression Data;Functional Module;Module Component;Final Module
日期:	2010
上传时间:	2019-03-06 15:19:34 (UTC+8)
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 hu b-attachment based method to detect functional modules from confidence-scored protein inte ractions and expression pr ofiles, 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.
關聯:	BMC Bioinformatics vol. 11, sup. 1
显示于类别:	[資訊工程學系] 期刊論文

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