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A binary matrix factorization algorithm for protein complex prediction
BACKGROUND: Identifying biologically relevant protein complexes from a large protein-protein interaction (PPI) network, is essential to understand the organization of biological systems. However, high-throughput experimental techniques that can produce a large amount of PPIs are known to yield non-n...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2011
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3724484/ https://www.ncbi.nlm.nih.gov/pubmed/22166008 http://dx.doi.org/10.1186/1477-5956-9-S1-S18 |
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author | Tu, Shikui Chen, Runsheng Xu, Lei |
author_facet | Tu, Shikui Chen, Runsheng Xu, Lei |
author_sort | Tu, Shikui |
collection | PubMed |
description | BACKGROUND: Identifying biologically relevant protein complexes from a large protein-protein interaction (PPI) network, is essential to understand the organization of biological systems. However, high-throughput experimental techniques that can produce a large amount of PPIs are known to yield non-negligible rates of false-positives and false-negatives, making the protein complexes difficult to be identified. RESULTS: We propose a binary matrix factorization (BMF) algorithm under the Bayesian Ying-Yang (BYY) harmony learning, to detect protein complexes by clustering the proteins which share similar interactions through factorizing the binary adjacent matrix of a PPI network. The proposed BYY-BMF algorithm automatically determines the cluster number while this number is pre-given for most existing BMF algorithms. Also, BYY-BMF’s clustering results does not depend on any parameters or thresholds, unlike the Markov Cluster Algorithm (MCL) that relies on a so-called inflation parameter. On synthetic PPI networks, the predictions evaluated by the known annotated complexes indicate that BYY-BMF is more robust than MCL for most cases. On real PPI networks from the MIPS and DIP databases, BYY-BMF obtains a better balanced prediction accuracies than MCL and a spectral analysis method, while MCL has its own advantages, e.g., with good separation values. |
format | Online Article Text |
id | pubmed-3724484 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2011 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-37244842013-07-29 A binary matrix factorization algorithm for protein complex prediction Tu, Shikui Chen, Runsheng Xu, Lei Proteome Sci Proceedings BACKGROUND: Identifying biologically relevant protein complexes from a large protein-protein interaction (PPI) network, is essential to understand the organization of biological systems. However, high-throughput experimental techniques that can produce a large amount of PPIs are known to yield non-negligible rates of false-positives and false-negatives, making the protein complexes difficult to be identified. RESULTS: We propose a binary matrix factorization (BMF) algorithm under the Bayesian Ying-Yang (BYY) harmony learning, to detect protein complexes by clustering the proteins which share similar interactions through factorizing the binary adjacent matrix of a PPI network. The proposed BYY-BMF algorithm automatically determines the cluster number while this number is pre-given for most existing BMF algorithms. Also, BYY-BMF’s clustering results does not depend on any parameters or thresholds, unlike the Markov Cluster Algorithm (MCL) that relies on a so-called inflation parameter. On synthetic PPI networks, the predictions evaluated by the known annotated complexes indicate that BYY-BMF is more robust than MCL for most cases. On real PPI networks from the MIPS and DIP databases, BYY-BMF obtains a better balanced prediction accuracies than MCL and a spectral analysis method, while MCL has its own advantages, e.g., with good separation values. BioMed Central 2011-10-14 /pmc/articles/PMC3724484/ /pubmed/22166008 http://dx.doi.org/10.1186/1477-5956-9-S1-S18 Text en Copyright ©2011 Tu et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Proceedings Tu, Shikui Chen, Runsheng Xu, Lei A binary matrix factorization algorithm for protein complex prediction |
title | A binary matrix factorization algorithm for protein complex prediction |
title_full | A binary matrix factorization algorithm for protein complex prediction |
title_fullStr | A binary matrix factorization algorithm for protein complex prediction |
title_full_unstemmed | A binary matrix factorization algorithm for protein complex prediction |
title_short | A binary matrix factorization algorithm for protein complex prediction |
title_sort | binary matrix factorization algorithm for protein complex prediction |
topic | Proceedings |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3724484/ https://www.ncbi.nlm.nih.gov/pubmed/22166008 http://dx.doi.org/10.1186/1477-5956-9-S1-S18 |
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