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Reconstruction of human protein interolog network using evolutionary conserved network

BACKGROUND: The recent increase in the use of high-throughput two-hybrid analysis has generated large quantities of data on protein interactions. Specifically, the availability of information about experimental protein-protein interactions and other protein features on the Internet enables human pro...

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Autores principales: Huang, Tao-Wei, Lin, Chung-Yen, Kao, Cheng-Yan
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2007
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1885812/
https://www.ncbi.nlm.nih.gov/pubmed/17493278
http://dx.doi.org/10.1186/1471-2105-8-152
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author Huang, Tao-Wei
Lin, Chung-Yen
Kao, Cheng-Yan
author_facet Huang, Tao-Wei
Lin, Chung-Yen
Kao, Cheng-Yan
author_sort Huang, Tao-Wei
collection PubMed
description BACKGROUND: The recent increase in the use of high-throughput two-hybrid analysis has generated large quantities of data on protein interactions. Specifically, the availability of information about experimental protein-protein interactions and other protein features on the Internet enables human protein-protein interactions to be computationally predicted from co-evolution events (interolog). This study also considers other protein interaction features, including sub-cellular localization, tissue-specificity, the cell-cycle stage and domain-domain combination. Computational methods need to be developed to integrate these heterogeneous biological data to facilitate the maximum accuracy of the human protein interaction prediction. RESULTS: This study proposes a relative conservation score by finding maximal quasi-cliques in protein interaction networks, and considering other interaction features to formulate a scoring method. The scoring method can be adopted to discover which protein pairs are the most likely to interact among multiple protein pairs. The predicted human protein-protein interactions associated with confidence scores are derived from six eukaryotic organisms – rat, mouse, fly, worm, thale cress and baker's yeast. CONCLUSION: Evaluation results of the proposed method using functional keyword and Gene Ontology (GO) annotations indicate that some confidence is justified in the accuracy of the predicted interactions. Comparisons among existing methods also reveal that the proposed method predicts human protein-protein interactions more accurately than other interolog-based methods.
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spelling pubmed-18858122007-06-04 Reconstruction of human protein interolog network using evolutionary conserved network Huang, Tao-Wei Lin, Chung-Yen Kao, Cheng-Yan BMC Bioinformatics Methodology Article BACKGROUND: The recent increase in the use of high-throughput two-hybrid analysis has generated large quantities of data on protein interactions. Specifically, the availability of information about experimental protein-protein interactions and other protein features on the Internet enables human protein-protein interactions to be computationally predicted from co-evolution events (interolog). This study also considers other protein interaction features, including sub-cellular localization, tissue-specificity, the cell-cycle stage and domain-domain combination. Computational methods need to be developed to integrate these heterogeneous biological data to facilitate the maximum accuracy of the human protein interaction prediction. RESULTS: This study proposes a relative conservation score by finding maximal quasi-cliques in protein interaction networks, and considering other interaction features to formulate a scoring method. The scoring method can be adopted to discover which protein pairs are the most likely to interact among multiple protein pairs. The predicted human protein-protein interactions associated with confidence scores are derived from six eukaryotic organisms – rat, mouse, fly, worm, thale cress and baker's yeast. CONCLUSION: Evaluation results of the proposed method using functional keyword and Gene Ontology (GO) annotations indicate that some confidence is justified in the accuracy of the predicted interactions. Comparisons among existing methods also reveal that the proposed method predicts human protein-protein interactions more accurately than other interolog-based methods. BioMed Central 2007-05-10 /pmc/articles/PMC1885812/ /pubmed/17493278 http://dx.doi.org/10.1186/1471-2105-8-152 Text en Copyright © 2007 Huang 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 Methodology Article
Huang, Tao-Wei
Lin, Chung-Yen
Kao, Cheng-Yan
Reconstruction of human protein interolog network using evolutionary conserved network
title Reconstruction of human protein interolog network using evolutionary conserved network
title_full Reconstruction of human protein interolog network using evolutionary conserved network
title_fullStr Reconstruction of human protein interolog network using evolutionary conserved network
title_full_unstemmed Reconstruction of human protein interolog network using evolutionary conserved network
title_short Reconstruction of human protein interolog network using evolutionary conserved network
title_sort reconstruction of human protein interolog network using evolutionary conserved network
topic Methodology Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1885812/
https://www.ncbi.nlm.nih.gov/pubmed/17493278
http://dx.doi.org/10.1186/1471-2105-8-152
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