NDLI: Module detection for bacteria based on spectral clustering of protein-protein functional association networks

Content Provider	IEEE Xplore Digital Library
Author	Hongwei Wu Yaming Lin Fun Choi Chan Alba-Flores, R.
Copyright Year	2011
Description	Author affiliation: Department of Mechanical and Electrical Engineering, Georgia Southern University, Statesboro, GA, USA (Alba-Flores, R.) \|\| Department of Mathematical Sciences, Georgia Southern University, Statesboro, GA, USA (Fun Choi Chan) \|\| School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA (Hongwei Wu; Yaming Lin)
Abstract	Network analysis-based module detection has significant implications in many fields. In cellular/ molecular biology, module detection based on analyses of metabolic/regulatory networks will not only help us understand more about the function and evolution of cellular machinery of an organism, but will also provide tractable contextual information for potential drug targets and facilitate improvements in drug designs. We here present our preliminary study on the module detection for bacteria based on the spectral clustering of the protein-protein functional association networks. We first examined how the parameter of the spectral clustering algorithm (i.e., the number of clusters) affects our module detection results, and demonstrated that when the number of clusters was set too small or too large the resulting module collection deteriorate in terms of gene coverage and intra-module association. We then compared our predicted modules against the randomly generated modules, and demonstrated that our modules (i) have a higher ratio of the intra-module to inter-module gene-gene functional association scores and (ii) can better capture the modularization information inherent in the experimentally verified modules. Finally we compared the module collections of seven bacterial organisms, and observed that modules related to membrane transport and cell motility are among those that are conserved among multiple organisms. Because it is desirable from both scientific and technical points of view to study functional modules at various resolution levels, we believe that the spectral clustering algorithm, with the flexibility rendered by different parameter settings, provides an appropriate solution in terms of capturing the modularization properties of networks and computational affordability.
Starting Page	465
Ending Page	472
File Size	1073106
Page Count	8
File Format	PDF
ISBN	9781457716126
e-ISBN	9781457716133
DOI	10.1109/BIBMW.2011.6112415
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-11-12
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Proteins Laplace equations Clustering algorithms Genomics Functional Association Network Organisms Inference algorithms Spectral clustering Module Bioinformatics
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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