Document Detail


Supervised maximum-likelihood weighting of composite protein networks for complex prediction.
MedLine Citation:
PMID:  23281936     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
BACKGROUND: Protein complexes participate in many important cellular functions, so finding the set of existent complexes is essential for understanding the organization and regulation of processes in the cell. With the availability of large amounts of high-throughput protein-protein interaction (PPI) data, many algorithms have been proposed to discover protein complexes from PPI networks. However, such approaches are hindered by the high rate of noise in high-throughput PPI data, including spurious and missing interactions. Furthermore, many transient interactions are detected between proteins that are not from the same complex, while not all proteins from the same complex may actually interact. As a result, predicted complexes often do not match true complexes well, and many true complexes go undetected.
RESULTS: We address these challenges by integrating PPI data with other heterogeneous data sources to construct a composite protein network, and using a supervised maximum-likelihood approach to weight each edge based on its posterior probability of belonging to a complex. We then use six different clustering algorithms, and an aggregative clustering strategy, to discover complexes in the weighted network. We test our method on Saccharomyces cerevisiae and Homo sapiens, and show that complex discovery is improved: compared to previously proposed supervised and unsupervised weighting approaches, our method recalls more known complexes, achieves higher precision at all recall levels, and generates novel complexes of greater functional similarity. Furthermore, our maximum-likelihood approach allows learned parameters to be used to visualize and evaluate the evidence of novel predictions, aiding human judgment of their credibility.
CONCLUSIONS: Our approach integrates multiple data sources with supervised learning to create a weighted composite protein network, and uses six clustering algorithms with an aggregative clustering strategy to discover novel complexes. We show improved performance over previous approaches in terms of precision, recall, and number and quality of novel predictions. We present and visualize two novel predicted complexes in yeast and human, and find external evidence supporting these predictions.
Authors:
Chern Han Yong; Guimei Liu; Hon Nian Chua; Limsoon Wong
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Publication Detail:
Type:  Journal Article; Research Support, Non-U.S. Gov't     Date:  2012-12-12
Journal Detail:
Title:  BMC systems biology     Volume:  6 Suppl 2     ISSN:  1752-0509     ISO Abbreviation:  BMC Syst Biol     Publication Date:  2012  
Date Detail:
Created Date:  2013-01-03     Completed Date:  2013-06-06     Revised Date:  2013-07-11    
Medline Journal Info:
Nlm Unique ID:  101301827     Medline TA:  BMC Syst Biol     Country:  England    
Other Details:
Languages:  eng     Pagination:  S13     Citation Subset:  IM    
Affiliation:
Graduate School for Integrative Sciences and Engineering, National University of Singapore, Singapore. cherny@nus.edu.sg
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MeSH Terms
Descriptor/Qualifier:
BRCA1 Protein / metabolism
Bayes Theorem
Cluster Analysis
Computational Biology / methods*
Humans
Likelihood Functions
Protein Interaction Maps*
Saccharomyces cerevisiae Proteins / metabolism
Chemical
Reg. No./Substance:
0/BRCA1 Protein; 0/Saccharomyces cerevisiae Proteins
Comments/Corrections

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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