DMCA
An ensemble framework for clustering protein-protein interaction networks (2007)
| Venue: | In Proc. 15th Annual Int’l Conference on Intelligent Systems for Molecular Biology (ISMB |
| Citations: | 47 - 4 self |
BibTeX
@INPROCEEDINGS{Asur07anensemble,
author = {Sitaram Asur and Srinivasan Parthasarathy and Duygu Ucar},
title = {An ensemble framework for clustering protein-protein interaction networks},
booktitle = {In Proc. 15th Annual Int’l Conference on Intelligent Systems for Molecular Biology (ISMB},
year = {2007},
pages = {2007}
}
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Abstract
Protein-Protein Interaction (PPI) networks are believed to be important sources of information related to biological processes and complex metabolic functions of the cell. The presence of biologically relevant functional modules in these networks has been theorized by many researchers. However, the application of traditional clustering algorithms for extracting these modules has not been successful, largely due to the presence of noisy false positive interactions as well as specific topological challenges in the network. In this paper, we propose an ensemble clustering framework to address this problem. For base clustering, we introduce two topology-based distance metrics to counteract the effects of noise. We develop a PCA-based consensus clustering technique, designed to reduce the dimensionality of the consensus problem and yield informative clusters. We also develop a soft consensus clustering variant to assign multifaceted proteins to multiple functional groups. We conduct an empirical evaluation of different consensus techniques using topology-based, information theoretic and domain-specific validation metrics and show that our approaches can provide significant benefits over other state-of-theart approaches. Our analysis of the consensus clusters obtained demonstrates that ensemble clustering can a) produce improved biologically significant functional groupings; and b) facilitate soft clustering by discovering multiple functional associations for proteins. 1.
Keyphrases
ensemble framework protein-protein interaction network consensus cluster biological process topology-based distance metric multiple functional association pca-based consensus clustering technique significant functional grouping multifaceted protein significant benefit specific topological challenge different consensus technique relevant functional module ensemble clustering ensemble clustering framework consensus problem facilitate soft clustering traditional clustering algorithm important source soft consensus complex metabolic function yield informative cluster state-of-theart approach base clustering functional group protein-protein interaction domain-specific validation metric noisy false positive interaction many researcher empirical evaluation
