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Heterogeneous network embedding enabling accurate disease association predictions
BACKGROUND: It is significant to identificate complex biological mechanisms of various diseases in biomedical research. Recently, the growing generation of tremendous amount of data in genomics, epigenomics, metagenomics, proteomics, metabolomics, nutriomics, etc., has resulted in the rise of system...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6927100/ https://www.ncbi.nlm.nih.gov/pubmed/31865913 http://dx.doi.org/10.1186/s12920-019-0623-3 |
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author | Xiong, Yun Guo, Mengjie Ruan, Lu Kong, Xiangnan Tang, Chunlei Zhu, Yangyong Wang, Wei |
author_facet | Xiong, Yun Guo, Mengjie Ruan, Lu Kong, Xiangnan Tang, Chunlei Zhu, Yangyong Wang, Wei |
author_sort | Xiong, Yun |
collection | PubMed |
description | BACKGROUND: It is significant to identificate complex biological mechanisms of various diseases in biomedical research. Recently, the growing generation of tremendous amount of data in genomics, epigenomics, metagenomics, proteomics, metabolomics, nutriomics, etc., has resulted in the rise of systematic biological means of exploring complex diseases. However, the disparity between the production of the multiple data and our capability of analyzing data has been broaden gradually. Furthermore, we observe that networks can represent many of the above-mentioned data, and founded on the vector representations learned by network embedding methods, entities which are in close proximity but at present do not actually possess direct links are very likely to be related, therefore they are promising candidate subjects for biological investigation. RESULTS: We incorporate six public biological databases to construct a heterogeneous biological network containing three categories of entities (i.e., genes, diseases, miRNAs) and multiple types of edges (i.e., the known relationships). To tackle the inherent heterogeneity, we develop a heterogeneous network embedding model for mapping the network into a low dimensional vector space in which the relationships between entities are preserved well. And in order to assess the effectiveness of our method, we conduct gene-disease as well as miRNA-disease associations predictions, results of which show the superiority of our novel method over several state-of-the-arts. Furthermore, many associations predicted by our method are verified in the latest real-world dataset. CONCLUSIONS: We propose a novel heterogeneous network embedding method which can adequately take advantage of the abundant contextual information and structures of heterogeneous network. Moreover, we illustrate the performance of the proposed method on directing studies in biology, which can assist in identifying new hypotheses in biological investigation. |
format | Online Article Text |
id | pubmed-6927100 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-69271002019-12-30 Heterogeneous network embedding enabling accurate disease association predictions Xiong, Yun Guo, Mengjie Ruan, Lu Kong, Xiangnan Tang, Chunlei Zhu, Yangyong Wang, Wei BMC Med Genomics Research BACKGROUND: It is significant to identificate complex biological mechanisms of various diseases in biomedical research. Recently, the growing generation of tremendous amount of data in genomics, epigenomics, metagenomics, proteomics, metabolomics, nutriomics, etc., has resulted in the rise of systematic biological means of exploring complex diseases. However, the disparity between the production of the multiple data and our capability of analyzing data has been broaden gradually. Furthermore, we observe that networks can represent many of the above-mentioned data, and founded on the vector representations learned by network embedding methods, entities which are in close proximity but at present do not actually possess direct links are very likely to be related, therefore they are promising candidate subjects for biological investigation. RESULTS: We incorporate six public biological databases to construct a heterogeneous biological network containing three categories of entities (i.e., genes, diseases, miRNAs) and multiple types of edges (i.e., the known relationships). To tackle the inherent heterogeneity, we develop a heterogeneous network embedding model for mapping the network into a low dimensional vector space in which the relationships between entities are preserved well. And in order to assess the effectiveness of our method, we conduct gene-disease as well as miRNA-disease associations predictions, results of which show the superiority of our novel method over several state-of-the-arts. Furthermore, many associations predicted by our method are verified in the latest real-world dataset. CONCLUSIONS: We propose a novel heterogeneous network embedding method which can adequately take advantage of the abundant contextual information and structures of heterogeneous network. Moreover, we illustrate the performance of the proposed method on directing studies in biology, which can assist in identifying new hypotheses in biological investigation. BioMed Central 2019-12-23 /pmc/articles/PMC6927100/ /pubmed/31865913 http://dx.doi.org/10.1186/s12920-019-0623-3 Text en © The Author(s) 2019 Open Access This 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 Xiong, Yun Guo, Mengjie Ruan, Lu Kong, Xiangnan Tang, Chunlei Zhu, Yangyong Wang, Wei Heterogeneous network embedding enabling accurate disease association predictions |
title | Heterogeneous network embedding enabling accurate disease association predictions |
title_full | Heterogeneous network embedding enabling accurate disease association predictions |
title_fullStr | Heterogeneous network embedding enabling accurate disease association predictions |
title_full_unstemmed | Heterogeneous network embedding enabling accurate disease association predictions |
title_short | Heterogeneous network embedding enabling accurate disease association predictions |
title_sort | heterogeneous network embedding enabling accurate disease association predictions |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6927100/ https://www.ncbi.nlm.nih.gov/pubmed/31865913 http://dx.doi.org/10.1186/s12920-019-0623-3 |
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