NDLI: A Fast Hierarchical Clustering Algorithm for Functional Modules Discovery in Protein Interaction Networks

Content Provider	ACM Digital Library
Author	Li, Min Chen, Jianer Pan, Yi Wang, Jianxin
Abstract	As advances in the technologies of predicting protein interactions, huge data sets portrayed as networks have been available. Identification of functional modules from such networks is crucial for understanding principles of cellular organization and functions. However, protein interaction data produced by high-throughput experiments are generally associated with high false positives, which makes it difficult to identify functional modules accurately. In this paper, we propose a fast hierarchical clustering algorithm HC-PIN based on the local metric of edge clustering value which can be used both in the unweighted network and in the weighted network. The proposed algorithm HC-PIN is applied to the yeast protein interaction network, and the identified modules are validated by all the three types of Gene Ontology (GO) Terms: Biological Process, Molecular Function, and Cellular Component. The experimental results show that HC-PIN is not only robust to false positives, but also can discover the functional modules with low density. The identified modules are statistically significant in terms of three types of GO annotations. Moreover, HC-PIN can uncover the hierarchical organization of functional modules with the variation of its parameter's value, which is approximatively corresponding to the hierarchical structure of GO annotations. Compared to other previous competing algorithms, our algorithm HC-PIN is faster and more accurate.
Starting Page	607
Ending Page	620
Page Count	14
File Format	PDF
ISSN	15455963
DOI	10.1109/TCBB.2010.75
Volume Number	8
Issue Number	3
Journal	IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2011-05-01
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Protein interaction network, functional module, hierarchical clustering algorithm, Gene Ontology.
Content Type	Text
Resource Type	Article
Subject	Genetics Biotechnology Applied Mathematics

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