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Analyzing a co-occurrence gene-interaction network to identify disease-gene association
BACKGROUND: Understanding the genetic networks and their role in chronic diseases (e.g., cancer) is one of the important objectives of biological researchers. In this work, we present a text mining system that constructs a gene-gene-interaction network for the entire human genome and then performs n...
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/PMC6368766/ https://www.ncbi.nlm.nih.gov/pubmed/30736752 http://dx.doi.org/10.1186/s12859-019-2634-7 |
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author | Al-Aamri, Amira Taha, Kamal Al-Hammadi, Yousof Maalouf, Maher Homouz, Dirar |
author_facet | Al-Aamri, Amira Taha, Kamal Al-Hammadi, Yousof Maalouf, Maher Homouz, Dirar |
author_sort | Al-Aamri, Amira |
collection | PubMed |
description | BACKGROUND: Understanding the genetic networks and their role in chronic diseases (e.g., cancer) is one of the important objectives of biological researchers. In this work, we present a text mining system that constructs a gene-gene-interaction network for the entire human genome and then performs network analysis to identify disease-related genes. We recognize the interacting genes based on their co-occurrence frequency within the biomedical literature and by employing linear and non-linear rare-event classification models. We analyze the constructed network of genes by using different network centrality measures to decide on the importance of each gene. Specifically, we apply betweenness, closeness, eigenvector, and degree centrality metrics to rank the central genes of the network and to identify possible cancer-related genes. RESULTS: We evaluated the top 15 ranked genes for different cancer types (i.e., Prostate, Breast, and Lung Cancer). The average precisions for identifying breast, prostate, and lung cancer genes vary between 80-100%. On a prostate case study, the system predicted an average of 80% prostate-related genes. CONCLUSIONS: The results show that our system has the potential for improving the prediction accuracy of identifying gene-gene interaction and disease-gene associations. We also conduct a prostate cancer case study by using the threshold property in logistic regression, and we compare our approach with some of the state-of-the-art methods. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12859-019-2634-7) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-6368766 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-63687662019-02-15 Analyzing a co-occurrence gene-interaction network to identify disease-gene association Al-Aamri, Amira Taha, Kamal Al-Hammadi, Yousof Maalouf, Maher Homouz, Dirar BMC Bioinformatics Methodology Article BACKGROUND: Understanding the genetic networks and their role in chronic diseases (e.g., cancer) is one of the important objectives of biological researchers. In this work, we present a text mining system that constructs a gene-gene-interaction network for the entire human genome and then performs network analysis to identify disease-related genes. We recognize the interacting genes based on their co-occurrence frequency within the biomedical literature and by employing linear and non-linear rare-event classification models. We analyze the constructed network of genes by using different network centrality measures to decide on the importance of each gene. Specifically, we apply betweenness, closeness, eigenvector, and degree centrality metrics to rank the central genes of the network and to identify possible cancer-related genes. RESULTS: We evaluated the top 15 ranked genes for different cancer types (i.e., Prostate, Breast, and Lung Cancer). The average precisions for identifying breast, prostate, and lung cancer genes vary between 80-100%. On a prostate case study, the system predicted an average of 80% prostate-related genes. CONCLUSIONS: The results show that our system has the potential for improving the prediction accuracy of identifying gene-gene interaction and disease-gene associations. We also conduct a prostate cancer case study by using the threshold property in logistic regression, and we compare our approach with some of the state-of-the-art methods. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12859-019-2634-7) contains supplementary material, which is available to authorized users. BioMed Central 2019-02-08 /pmc/articles/PMC6368766/ /pubmed/30736752 http://dx.doi.org/10.1186/s12859-019-2634-7 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 | Methodology Article Al-Aamri, Amira Taha, Kamal Al-Hammadi, Yousof Maalouf, Maher Homouz, Dirar Analyzing a co-occurrence gene-interaction network to identify disease-gene association |
title | Analyzing a co-occurrence gene-interaction network to identify disease-gene association |
title_full | Analyzing a co-occurrence gene-interaction network to identify disease-gene association |
title_fullStr | Analyzing a co-occurrence gene-interaction network to identify disease-gene association |
title_full_unstemmed | Analyzing a co-occurrence gene-interaction network to identify disease-gene association |
title_short | Analyzing a co-occurrence gene-interaction network to identify disease-gene association |
title_sort | analyzing a co-occurrence gene-interaction network to identify disease-gene association |
topic | Methodology Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6368766/ https://www.ncbi.nlm.nih.gov/pubmed/30736752 http://dx.doi.org/10.1186/s12859-019-2634-7 |
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