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.…
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| Author details: | Sara OmranianORCiDGND, Zoran NikoloskiORCiDGND |
|---|---|
| URN: | urn:nbn:de:kobv:517-opus4-586863 |
| DOI: | https://doi.org/10.25932/publishup-58686 |
| ISSN: | 1866-8372 |
| Title of parent work (German): | Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe |
| Publication series (Volume number): | Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe (1315) |
| Publication type: | Postprint |
| Language: | English |
| Date of first publication: | 2022/10/11 |
| Publication year: | 2022 |
| Publishing institution: | Universität Potsdam |
| Release date: | 2023/04/03 |
| Tag: | Network clustering; Protein complexes; Protein–protein interaction; Species comparison |
| Issue: | 1315 |
| Number of pages: | 12 |
| Source: | Applied Network Science 7 (2022), 71, DOI: 10.1007/s41109-022-00508-5 |
| Organizational units: | Mathematisch-Naturwissenschaftliche Fakultät / Institut für Biochemie und Biologie |
| DDC classification: | 3 Sozialwissenschaften / 30 Sozialwissenschaften, Soziologie / 300 Sozialwissenschaften |
| Peer review: | Referiert |
| Publishing method: | Open Access / Green Open-Access |
| License (German): |  CC-BY - Namensnennung 4.0 International |
| External remark: | Bibliographieeintrag der Originalveröffentlichung |
