This article is part of the supplement: Selected articles from the Eighth Asia-Pacific Bioinformatics Conference (APBC 2010)
A hub-attachment based method to detect functional modules from confidence-scored protein interactions and expression profiles
1 Institute of Information Science, Academia Sinica, No. 128 Yan-Chiu-Yuan Rd., Sec. 2, Taipei 115, Taiwan
2 Division of Biostatistics and Bioinformatics, National Health Research Institutes. No. 35 Keyan Rd. Zhunan, Miaoli County 350, Taiwan
3 Institute of Fishery Science, College of Life Science, National Taiwan University, No. 1, Roosevelt Rd. Sec 4, Taipei, Taiwan
4 Department of Computer Science and Information Engineering, National Central University, No.300, Jung-da Rd, Chung-li, Tao-yuan 320, Taiwan
5 Research Center of Information Technology Innovation, Academia Sinica, No. 128 Yan-Chiu-Yuan Rd., Sec. 2, Taipei 115, Taiwan
Citation and License
BMC Bioinformatics 2010, 11(Suppl 1):S25 doi:10.1186/1471-2105-11-S1-S25Published: 18 January 2010
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.
In this paper, we propose a novel
A C++ implementation of our prediction method, dataset and supplementary material are available at http://hub.iis.sinica.edu.tw/Hunter/ webcite. 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.