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This article is part of the supplement: Selected articles from the Eighth Asia-Pacific Bioinformatics Conference (APBC 2010)

Open Access Research

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

Chia-Hao Chin14, Shu-Hwa Chen1, Chin-Wen Ho4, Ming-Tat Ko15* and Chung-Yen Lin1235*

Author affiliations

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

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Citation and License

BMC Bioinformatics 2010, 11(Suppl 1):S25  doi:10.1186/1471-2105-11-S1-S25

Published: 18 January 2010

Abstract

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-attachme
    nt
based method to detect functional modules from confidence-scored protein int
    e
ractions and expression p
    r
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/ 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.