Please use this identifier to cite or link to this item:
https://doi.org/10.1186/1471-2105-10-169
DC Field | Value | |
---|---|---|
dc.title | A core-attachment based method to detect protein complexes in PPI networks | |
dc.contributor.author | Wu, M | |
dc.contributor.author | Li, X | |
dc.contributor.author | Kwoh, C.-K | |
dc.contributor.author | Ng, S.-K | |
dc.date.accessioned | 2020-10-20T04:38:56Z | |
dc.date.available | 2020-10-20T04:38:56Z | |
dc.date.issued | 2009 | |
dc.identifier.citation | Wu, 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.issn | 14712105 | |
dc.identifier.uri | https://scholarbank.nus.edu.sg/handle/10635/177953 | |
dc.description.abstract | Background: 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.rights | Attribution 4.0 International | |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | |
dc.source | Unpaywall 20201031 | |
dc.subject | Biological studies | |
dc.subject | Comprehensive comparisons | |
dc.subject | Core-attachment structures | |
dc.subject | Dense region | |
dc.subject | Ppi networks | |
dc.subject | Protein complexes | |
dc.subject | Protein-protein interactions | |
dc.subject | State-of-the-art techniques | |
dc.subject | Proteins | |
dc.subject | Complex networks | |
dc.subject | algorithm | |
dc.subject | article | |
dc.subject | complex formation | |
dc.subject | computer prediction | |
dc.subject | controlled study | |
dc.subject | data mining | |
dc.subject | information processing | |
dc.subject | intermethod comparison | |
dc.subject | protein analysis | |
dc.subject | protein protein interaction | |
dc.subject | proteomics | |
dc.subject | Saccharomyces cerevisiae | |
dc.subject | chemistry | |
dc.subject | computer program | |
dc.subject | metabolism | |
dc.subject | methodology | |
dc.subject | protein analysis | |
dc.subject | protein database | |
dc.subject | protein domain | |
dc.subject | reproducibility | |
dc.subject | statistical analysis | |
dc.subject | multiprotein complex | |
dc.subject | protein | |
dc.subject | Algorithms | |
dc.subject | Data Interpretation, Statistical | |
dc.subject | Databases, Protein | |
dc.subject | Multiprotein Complexes | |
dc.subject | Protein Interaction Domains and Motifs | |
dc.subject | Protein Interaction Mapping | |
dc.subject | Proteins | |
dc.subject | Reproducibility of Results | |
dc.subject | Software | |
dc.type | Article | |
dc.contributor.department | DEPARTMENT OF COMPUTER SCIENCE | |
dc.description.doi | 10.1186/1471-2105-10-169 | |
dc.description.sourcetitle | BMC Bioinformatics | |
dc.description.volume | 10 | |
dc.description.page | 169 | |
Appears in Collections: | Elements Staff Publications |
Show simple item record
Files in This Item:
File | Description | Size | Format | Access Settings | Version | |
---|---|---|---|---|---|---|
10_1186_1471-2105-10-169.pdf | 1.74 MB | Adobe PDF | OPEN | None | View/Download |
This item is licensed under a Creative Commons License