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This article is part of the supplement: Proceedings of the 21st International Conference on Genome Informatics (GIW2010)

Open Access Research

Prior knowledge based mining functional modules from Yeast PPI networks with gene ontology

Liping Jing1 and Michael K Ng2*

Author Affiliations

1 School of Computer and Information Technology, Beijing Jiaotong University, Beijing, 100044, P.R. China

2 Centre for Mathematical Imaging and Vision, and Department of Mathematics, Hong Kong Baptist University, Kowloon Tong, Hong Kong, China

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BMC Bioinformatics 2010, 11(Suppl 11):S3  doi:10.1186/1471-2105-11-S11-S3

Published: 14 December 2010

Abstract

Background

In the literature, there are fruitful algorithmic approaches for identification functional modules in protein-protein interactions (PPI) networks. Because of accumulation of large-scale interaction data on multiple organisms and non-recording interaction data in the existing PPI database, it is still emergent to design novel computational techniques that can be able to correctly and scalably analyze interaction data sets. Indeed there are a number of large scale biological data sets providing indirect evidence for protein-protein interaction relationships.

Results

The main aim of this paper is to present a prior knowledge based mining strategy to identify functional modules from PPI networks with the aid of Gene Ontology. Higher similarity value in Gene Ontology means that two gene products are more functionally related to each other, so it is better to group such gene products into one functional module. We study (i) to encode the functional pairs into the existing PPI networks; and (ii) to use these functional pairs as pairwise constraints to supervise the existing functional module identification algorithms. Topology-based modularity metric and complex annotation in MIPs will be used to evaluate the identified functional modules by these two approaches.

Conclusions

The experimental results on Yeast PPI networks and GO have shown that the prior knowledge based learning methods perform better than the existing algorithms.