NDLI: Clustering based on multiple biological information: approach for predicting protein complexes

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Clustering based on multiple biological information: approach for predicting protein complexes

Content Provider	IET Digital Library
Author	Tang, Xiwei Feng, Qilong Wang, Jianxin He, Yiming Pan, Yi
Abstract	Protein complexes are a cornerstone of many biological processes. Protein–protein interaction (PPI) data enable a number of computational methods for predicting protein complexes. However, the insufficiency of the PPI data significantly lowers the accuracy of computational methods. In the current work, the authors develop a novel method named clustering based on multiple biological information (CMBI) to discover protein complexes via the integration of multiple biological resources including gene expression profiles, essential protein information and PPI data. First, CMBI defines the functional similarity of each pair of interacting proteins based on the edge-clustering coefficient and the Pearson correlation coefficient. Second, CMBI selects essential proteins as seeds to build the protein complexes. A redundancy-filtering procedure is performed to eliminate redundant complexes. In addition to the essential proteins, CMBI also uses other proteins as seeds to expand protein complexes. To check the performance of CMBI, the authors compare the complexes discovered by CMBI with the ones found by other techniques by matching the predicted complexes against the reference complexes. The authors use subsequently GO::TermFinder to analyse the complexes predicted by various methods. Finally, the effect of parameters T and R is investigated. The results from GO functional enrichment and matching analyses show that CMBI performs significantly better than the state-of-the-art methods.
Starting Page	223
Ending Page	230
Page Count	8
ISSN	17518849
Volume Number	7
e-ISSN	17518857
Issue Number	Issue 5, Oct (2013)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-syb/7/5
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-syb.2012.0052
Journal	IET Systems Biology
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Bioinformatics Biological Processes Biology And Medical Computing Biomolecular Interactions Biomolecular Structure, Configuration, Conformation, And Active Sites Charge Transfer Complexes Computational Method Correlation Method Edge-clustering Coefficient Filtering Theory Gene Expression Profile Genetics Genomic Technique Genomics GO Functional Enrichment GO-TermFinder Macromolecular Configuration (bonds, Dimensions) Matching Analysis Molecular Biophysics Multiple Biological Information Clustering Multiple Biological ReSource Pattern Clustering Pearson Correlation Coefficient Physics of Subcellular Structure Protein Complexes Protein-protein Interaction Proteins Redundancy-filtering Procedure
Content Type	Text
Resource Type	Article
Subject	Cell Biology Genetics Molecular Biology Modeling and Simulation Biotechnology

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