Academic Journal
From communities to protein complexes: A local community detection algorithm on PPI networks.
| العنوان: | From communities to protein complexes: A local community detection algorithm on PPI networks. |
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| المؤلفون: | Saharnaz Dilmaghani, Matthias R Brust, Carlos H C Ribeiro, Emmanuel Kieffer, Grégoire Danoy, Pascal Bouvry |
| المصدر: | PLoS ONE, Vol 17, Iss 1, p e0260484 (2022) |
| بيانات النشر: | Public Library of Science (PLoS) |
| سنة النشر: | 2022 |
| المجموعة: | Directory of Open Access Journals: DOAJ Articles |
| مصطلحات موضوعية: | Medicine, Science |
| الوصف: | Identifying protein complexes in protein-protein interaction (ppi) networks is often handled as a community detection problem, with algorithms generally relying exclusively on the network topology for discovering a solution. The advancement of experimental techniques on ppi has motivated the generation of many Gene Ontology (go) databases. Incorporating the functionality extracted from go with the topological properties from the underlying ppi network yield a novel approach to identify protein complexes. Additionally, most of the existing algorithms use global measures that operate on the entire network to identify communities. The result of using global metrics are large communities that are often not correlated with the functionality of the proteins. Moreover, ppi network analysis shows that most of the biological functions possibly lie between local neighbours in ppi networks, which are not identifiable with global metrics. In this paper, we propose a local community detection algorithm, (lcda-go), that uniquely exploits information of functionality from go combined with the network topology. lcda-go identifies the community of each protein based on the topological and functional knowledge acquired solely from the local neighbour proteins within the ppi network. Experimental results using the Krogan dataset demonstrate that our algorithm outperforms in most cases state-of-the-art approaches in assessment based on Precision, Sensitivity, and particularly Composite Score. We also deployed lcda, the local-topology based precursor of lcda-go, to compare with a similar state-of-the-art approach that exclusively incorporates topological information of ppi networks for community detection. In addition to the high quality of the results, one main advantage of lcda-go is its low computational time complexity. |
| نوع الوثيقة: | article in journal/newspaper |
| اللغة: | English |
| تدمد: | 1932-6203 |
| Relation: | https://doi.org/10.1371/journal.pone.0260484; https://doaj.org/toc/1932-6203; https://doaj.org/article/52dc66ca8d6a49d29a3529c0ec27ce89 |
| DOI: | 10.1371/journal.pone.0260484 |
| الاتاحة: | https://doi.org/10.1371/journal.pone.0260484 https://doaj.org/article/52dc66ca8d6a49d29a3529c0ec27ce89 |
| رقم الانضمام: | edsbas.F7F2E2E0 |
| قاعدة البيانات: | BASE |
Full Text Finder | تدمد: | 19326203 |
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| DOI: | 10.1371/journal.pone.0260484 |