Please use this identifier to cite or link to this item: https://doi.org/10.1186/1471-2164-11-S1-S3
Title: Computational approaches for detecting protein complexes from protein interaction networks: A survey
Authors: Li, X 
Wu, M
Kwoh, C
Ng, S
Keywords: accuracy
article
Bayesian learning
cluster analysis
data analysis software
data mining
density
fuzzy system
gene expression
genome
hidden Markov model
high throughput screening
information processing
mathematical analysis
nonhuman
prediction
probability
protein function
protein protein interaction
quality control
recall
reliability
sensitivity and specificity
simulation
statistical significance
biometry
gene expression profiling
human
metabolism
methodology
protein analysis
protein binding
systems biology
protein
Biometry
Gene Expression Profiling
Humans
Protein Binding
Protein Interaction Mapping
Proteins
Systems Biology
Issue Date: 2010
Citation: Li, X, Wu, M, Kwoh, C, Ng, S (2010). Computational approaches for detecting protein complexes from protein interaction networks: A survey. BMC Genomics 11 (SUPPL. 1) : S3. ScholarBank@NUS Repository. https://doi.org/10.1186/1471-2164-11-S1-S3
Rights: Attribution 4.0 International
Abstract: Background: Most proteins form macromolecular complexes to perform their biological functions. However, experimentally determined protein complex data, especially of those involving more than two protein partners, are relatively limited in the current state-of-the-art high-throughput experimental techniques. Nevertheless, many techniques (such as yeast-two-hybrid) have enabled systematic screening of pairwise protein-protein interactions en masse. Thus computational approaches for detecting protein complexes from protein interaction data are useful complements to the limited experimental methods. They can be used together with the experimental methods for mapping the interactions of proteins to understand how different proteins are organized into higher-level substructures to perform various cellular functions.Results: Given the abundance of pairwise protein interaction data from high-throughput genome-wide experimental screenings, a protein interaction network can be constructed from protein interaction data by considering individual proteins as the nodes, and the existence of a physical interaction between a pair of proteins as a link. This binary protein interaction graph can then be used for detecting protein complexes using graph clustering techniques. In this paper, we review and evaluate the state-of-the-art techniques for computational detection of protein complexes, and discuss some promising research directions in this field.Conclusions: Experimental results with yeast protein interaction data show that the interaction subgraphs discovered by various computational methods matched well with actual protein complexes. In addition, the computational approaches have also improved in performance over the years. Further improvements could be achieved if the quality of the underlying protein interaction data can be considered adequately to minimize the undesirable effects from the irrelevant and noisy sources, and the various biological evidences can be better incorporated into the detection process to maximize the exploitation of the increasing wealth of biological knowledge available. © 2010 Li et al; licensee BioMed Central Ltd.
Source Title: BMC Genomics
URI: https://scholarbank.nus.edu.sg/handle/10635/181678
ISSN: 14712164
DOI: 10.1186/1471-2164-11-S1-S3
Rights: Attribution 4.0 International
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