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Refine gene functional similarity network based on interaction networks

BACKGROUND: In recent years, biological interaction networks have become the basis of some essential study and achieved success in many applications. Some typical networks such as protein-protein interaction networks have already been investigated systematically. However, little work has been availa...

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Detalles Bibliográficos
Autores principales: Tian, Zhen, Guo, Maozu, Wang, Chunyu, Liu, Xiaoyan, Wang, Shiming
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5751769/
https://www.ncbi.nlm.nih.gov/pubmed/29297381
http://dx.doi.org/10.1186/s12859-017-1969-1
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author Tian, Zhen
Guo, Maozu
Wang, Chunyu
Liu, Xiaoyan
Wang, Shiming
author_facet Tian, Zhen
Guo, Maozu
Wang, Chunyu
Liu, Xiaoyan
Wang, Shiming
author_sort Tian, Zhen
collection PubMed
description BACKGROUND: In recent years, biological interaction networks have become the basis of some essential study and achieved success in many applications. Some typical networks such as protein-protein interaction networks have already been investigated systematically. However, little work has been available for the construction of gene functional similarity networks so far. In this research, we will try to build a high reliable gene functional similarity network to promote its further application. RESULTS: Here, we propose a novel method to construct and refine the gene functional similarity network. It mainly contains three steps. First, we establish an integrated gene functional similarity networks based on different functional similarity calculation methods. Then, we construct a referenced gene-gene association network based on the protein-protein interaction networks. At last, we refine the spurious edges in the integrated gene functional similarity network with the help of the referenced gene-gene association network. Experiment results indicate that the refined gene functional similarity network (RGFSN) exhibits a scale-free, small world and modular architecture, with its degrees fit best to power law distribution. In addition, we conduct protein complex prediction experiment for human based on RGFSN and achieve an outstanding result, which implies it has high reliability and wide application significance. CONCLUSIONS: Our efforts are insightful for constructing and refining gene functional similarity networks, which can be applied to build other high quality biological networks. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12859-017-1969-1) contains supplementary material, which is available to authorized users.
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spelling pubmed-57517692018-01-05 Refine gene functional similarity network based on interaction networks Tian, Zhen Guo, Maozu Wang, Chunyu Liu, Xiaoyan Wang, Shiming BMC Bioinformatics Research BACKGROUND: In recent years, biological interaction networks have become the basis of some essential study and achieved success in many applications. Some typical networks such as protein-protein interaction networks have already been investigated systematically. However, little work has been available for the construction of gene functional similarity networks so far. In this research, we will try to build a high reliable gene functional similarity network to promote its further application. RESULTS: Here, we propose a novel method to construct and refine the gene functional similarity network. It mainly contains three steps. First, we establish an integrated gene functional similarity networks based on different functional similarity calculation methods. Then, we construct a referenced gene-gene association network based on the protein-protein interaction networks. At last, we refine the spurious edges in the integrated gene functional similarity network with the help of the referenced gene-gene association network. Experiment results indicate that the refined gene functional similarity network (RGFSN) exhibits a scale-free, small world and modular architecture, with its degrees fit best to power law distribution. In addition, we conduct protein complex prediction experiment for human based on RGFSN and achieve an outstanding result, which implies it has high reliability and wide application significance. CONCLUSIONS: Our efforts are insightful for constructing and refining gene functional similarity networks, which can be applied to build other high quality biological networks. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12859-017-1969-1) contains supplementary material, which is available to authorized users. BioMed Central 2017-12-28 /pmc/articles/PMC5751769/ /pubmed/29297381 http://dx.doi.org/10.1186/s12859-017-1969-1 Text en © The Author(s). 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research
Tian, Zhen
Guo, Maozu
Wang, Chunyu
Liu, Xiaoyan
Wang, Shiming
Refine gene functional similarity network based on interaction networks
title Refine gene functional similarity network based on interaction networks
title_full Refine gene functional similarity network based on interaction networks
title_fullStr Refine gene functional similarity network based on interaction networks
title_full_unstemmed Refine gene functional similarity network based on interaction networks
title_short Refine gene functional similarity network based on interaction networks
title_sort refine gene functional similarity network based on interaction networks
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5751769/
https://www.ncbi.nlm.nih.gov/pubmed/29297381
http://dx.doi.org/10.1186/s12859-017-1969-1
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AT liuxiaoyan refinegenefunctionalsimilaritynetworkbasedoninteractionnetworks
AT wangshiming refinegenefunctionalsimilaritynetworkbasedoninteractionnetworks