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CUBCO+: prediction of protein complexes based on min-cut network partitioning into biclique spanned subgraphs

  • High-throughput proteomics approaches have resulted in large-scale protein–protein interaction (PPI) networks that have been employed for the prediction of protein complexes. However, PPI networks contain false-positive as well as false-negative PPIs that affect the protein complex prediction algorithms. To address this issue, here we propose an algorithm called CUBCO+ that: (1) employs GO semantic similarity to retain only biologically relevant interactions with a high similarity score, (2) based on link prediction approaches, scores the false-negative edges, and (3) incorporates the resulting scores to predict protein complexes. Through comprehensive analyses with PPIs from Escherichia coli, Saccharomyces cerevisiae, and Homo sapiens, we show that CUBCO+ performs as well as the approaches that predict protein complexes based on recently introduced graph partitions into biclique spanned subgraphs and outperforms the other state-of-the-art approaches. Moreover, we illustrate that in combination with GO semantic similarity,High-throughput proteomics approaches have resulted in large-scale protein–protein interaction (PPI) networks that have been employed for the prediction of protein complexes. However, PPI networks contain false-positive as well as false-negative PPIs that affect the protein complex prediction algorithms. To address this issue, here we propose an algorithm called CUBCO+ that: (1) employs GO semantic similarity to retain only biologically relevant interactions with a high similarity score, (2) based on link prediction approaches, scores the false-negative edges, and (3) incorporates the resulting scores to predict protein complexes. Through comprehensive analyses with PPIs from Escherichia coli, Saccharomyces cerevisiae, and Homo sapiens, we show that CUBCO+ performs as well as the approaches that predict protein complexes based on recently introduced graph partitions into biclique spanned subgraphs and outperforms the other state-of-the-art approaches. Moreover, we illustrate that in combination with GO semantic similarity, CUBCO+ enables us to predict more accurate protein complexes in 36% of the cases in comparison to CUBCO as its predecessor.show moreshow less

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Metadaten
Author details:Sara OmranianORCiDGND, Zoran NikoloskiORCiDGND
DOI:https://doi.org/10.1007/s41109-022-00508-5
ISSN:2364-8228
Title of parent work (English):Applied Network Science
Publisher:Springer International Publishing
Place of publishing:Cham
Publication type:Article
Language:English
Date of first publication:2022/10/11
Publication year:2022
Release date:2023/04/03
Tag:Network clustering; Protein complexes; Protein–protein interaction; Species comparison
Volume:7
Article number:71
Number of pages:12
Funding institution:Universität Potsdam
Funding institution:Deutsche Forschungsgemeinschaft (DFG)
Funding number:PA 2022_189
Funding number:Projektnummer 491466077
Organizational units:Mathematisch-Naturwissenschaftliche Fakultät / Institut für Biochemie und Biologie
DDC classification:3 Sozialwissenschaften / 30 Sozialwissenschaften, Soziologie / 300 Sozialwissenschaften
Peer review:Referiert
Grantor:Publikationsfonds der Universität Potsdam
Publishing method:Open Access / Gold Open-Access
License (German):License LogoCC-BY - Namensnennung 4.0 International
External remark:Zweitveröffentlichung in der Schriftenreihe Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe ; 1315
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