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Feature related multi-view nonnegative matrix factorization for identifying conserved functional modules in multiple biological networks
BACKGROUND: Comprehensive analyzing multi-omics biological data in different conditions is important for understanding biological mechanism in system level. Multiple or multi-layer network model gives us a new insight into simultaneously analyzing these data, for instance, to identify conserved func...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6206826/ https://www.ncbi.nlm.nih.gov/pubmed/30373534 http://dx.doi.org/10.1186/s12859-018-2434-5 |
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author | Wang, Peizhuo Gao, Lin Hu, Yuxuan Li, Feng |
author_facet | Wang, Peizhuo Gao, Lin Hu, Yuxuan Li, Feng |
author_sort | Wang, Peizhuo |
collection | PubMed |
description | BACKGROUND: Comprehensive analyzing multi-omics biological data in different conditions is important for understanding biological mechanism in system level. Multiple or multi-layer network model gives us a new insight into simultaneously analyzing these data, for instance, to identify conserved functional modules in multiple biological networks. However, because of the larger scale and more complicated structure of multiple networks than single network, how to accurate and efficient detect conserved functional biological modules remains a significant challenge. RESULTS: Here, we propose an efficient method, named ConMod, to discover conserved functional modules in multiple biological networks. We introduce two features to characterize multiple networks, thus all networks are compressed into two feature matrices. The module detection is only performed in the feature matrices by using multi-view non-negative matrix factorization (NMF), which is independent of the number of input networks. Experimental results on both synthetic and real biological networks demonstrate that our method is promising in identifying conserved modules in multiple networks since it improves the accuracy and efficiency comparing with state-of-the-art methods. Furthermore, applying ConMod to co-expression networks of different cancers, we find cancer shared gene modules, the majority of which have significantly functional implications, such as ribosome biogenesis and immune response. In addition, analyzing on brain tissue-specific protein interaction networks, we detect conserved modules related to nervous system development, mRNA processing, etc. CONCLUSIONS: ConMod facilitates finding conserved modules in any number of networks with a low time and space complexity, thereby serve as a valuable tool for inference shared traits and biological functions of multiple biological system. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12859-018-2434-5) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-6206826 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-62068262018-10-31 Feature related multi-view nonnegative matrix factorization for identifying conserved functional modules in multiple biological networks Wang, Peizhuo Gao, Lin Hu, Yuxuan Li, Feng BMC Bioinformatics Methodology Article BACKGROUND: Comprehensive analyzing multi-omics biological data in different conditions is important for understanding biological mechanism in system level. Multiple or multi-layer network model gives us a new insight into simultaneously analyzing these data, for instance, to identify conserved functional modules in multiple biological networks. However, because of the larger scale and more complicated structure of multiple networks than single network, how to accurate and efficient detect conserved functional biological modules remains a significant challenge. RESULTS: Here, we propose an efficient method, named ConMod, to discover conserved functional modules in multiple biological networks. We introduce two features to characterize multiple networks, thus all networks are compressed into two feature matrices. The module detection is only performed in the feature matrices by using multi-view non-negative matrix factorization (NMF), which is independent of the number of input networks. Experimental results on both synthetic and real biological networks demonstrate that our method is promising in identifying conserved modules in multiple networks since it improves the accuracy and efficiency comparing with state-of-the-art methods. Furthermore, applying ConMod to co-expression networks of different cancers, we find cancer shared gene modules, the majority of which have significantly functional implications, such as ribosome biogenesis and immune response. In addition, analyzing on brain tissue-specific protein interaction networks, we detect conserved modules related to nervous system development, mRNA processing, etc. CONCLUSIONS: ConMod facilitates finding conserved modules in any number of networks with a low time and space complexity, thereby serve as a valuable tool for inference shared traits and biological functions of multiple biological system. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12859-018-2434-5) contains supplementary material, which is available to authorized users. BioMed Central 2018-10-29 /pmc/articles/PMC6206826/ /pubmed/30373534 http://dx.doi.org/10.1186/s12859-018-2434-5 Text en © The Author(s). 2018 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 | Methodology Article Wang, Peizhuo Gao, Lin Hu, Yuxuan Li, Feng Feature related multi-view nonnegative matrix factorization for identifying conserved functional modules in multiple biological networks |
title | Feature related multi-view nonnegative matrix factorization for identifying conserved functional modules in multiple biological networks |
title_full | Feature related multi-view nonnegative matrix factorization for identifying conserved functional modules in multiple biological networks |
title_fullStr | Feature related multi-view nonnegative matrix factorization for identifying conserved functional modules in multiple biological networks |
title_full_unstemmed | Feature related multi-view nonnegative matrix factorization for identifying conserved functional modules in multiple biological networks |
title_short | Feature related multi-view nonnegative matrix factorization for identifying conserved functional modules in multiple biological networks |
title_sort | feature related multi-view nonnegative matrix factorization for identifying conserved functional modules in multiple biological networks |
topic | Methodology Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6206826/ https://www.ncbi.nlm.nih.gov/pubmed/30373534 http://dx.doi.org/10.1186/s12859-018-2434-5 |
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