Please use this identifier to cite or link to this item: https://doi.org/10.1186/1471-2105-10-169
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dc.titleA core-attachment based method to detect protein complexes in PPI networks
dc.contributor.authorWu, M
dc.contributor.authorLi, X
dc.contributor.authorKwoh, C.-K
dc.contributor.authorNg, S.-K
dc.date.accessioned2020-10-20T04:38:56Z
dc.date.available2020-10-20T04:38:56Z
dc.date.issued2009
dc.identifier.citationWu, M, Li, X, Kwoh, C.-K, Ng, S.-K (2009). A core-attachment based method to detect protein complexes in PPI networks. BMC Bioinformatics 10 : 169. ScholarBank@NUS Repository. https://doi.org/10.1186/1471-2105-10-169
dc.identifier.issn14712105
dc.identifier.urihttps://scholarbank.nus.edu.sg/handle/10635/177953
dc.description.abstractBackground: How to detect protein complexes is an important and challenging task in post genomic era. As the increasing amount of protein-protein interaction (PPI) data are available, we are able to identify protein complexes from PPI networks. However, most of current studies detect protein complexes based solely on the observation that dense regions in PPI networks may correspond to protein complexes, but fail to consider the inherent organization within protein complexes. Results: To provide insights into the organization of protein complexes, this paper presents a novel core-attachment based method (COACH) which detects protein complexes in two stages. It first detects protein-complex cores as the "hearts" of protein complexes and then includes attachments into these cores to form biologically meaningful structures. We evaluate and analyze our predicted protein complexes from two aspects. First, we perform a comprehensive comparison between our proposed method and existing techniques by comparing the predicted complexes against benchmark complexes. Second, we also validate the core-attachment structures using various biological evidence and knowledge. Conclusion: Our proposed COACH method has been applied on two different yeast PPI networks and the experimental results show that COACH performs significantly better than the state-of-the-art techniques. In addition, the identified complexes with core-attachment structures are demonstrated to match very well with existing biological knowledge and thus provide more insights for future biological study. © 2009 Wu et al; licensee BioMed Central Ltd.
dc.rightsAttribution 4.0 International
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.sourceUnpaywall 20201031
dc.subjectBiological studies
dc.subjectComprehensive comparisons
dc.subjectCore-attachment structures
dc.subjectDense region
dc.subjectPpi networks
dc.subjectProtein complexes
dc.subjectProtein-protein interactions
dc.subjectState-of-the-art techniques
dc.subjectProteins
dc.subjectComplex networks
dc.subjectalgorithm
dc.subjectarticle
dc.subjectcomplex formation
dc.subjectcomputer prediction
dc.subjectcontrolled study
dc.subjectdata mining
dc.subjectinformation processing
dc.subjectintermethod comparison
dc.subjectprotein analysis
dc.subjectprotein protein interaction
dc.subjectproteomics
dc.subjectSaccharomyces cerevisiae
dc.subjectchemistry
dc.subjectcomputer program
dc.subjectmetabolism
dc.subjectmethodology
dc.subjectprotein analysis
dc.subjectprotein database
dc.subjectprotein domain
dc.subjectreproducibility
dc.subjectstatistical analysis
dc.subjectmultiprotein complex
dc.subjectprotein
dc.subjectAlgorithms
dc.subjectData Interpretation, Statistical
dc.subjectDatabases, Protein
dc.subjectMultiprotein Complexes
dc.subjectProtein Interaction Domains and Motifs
dc.subjectProtein Interaction Mapping
dc.subjectProteins
dc.subjectReproducibility of Results
dc.subjectSoftware
dc.typeArticle
dc.contributor.departmentDEPARTMENT OF COMPUTER SCIENCE
dc.description.doi10.1186/1471-2105-10-169
dc.description.sourcetitleBMC Bioinformatics
dc.description.volume10
dc.description.page169
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