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Identifying Tumor-Associated Genes from Bilayer Networks of DNA Methylation Sites and RNAs
Network theory has attracted much attention from the biological community because of its high efficacy in identifying tumor-associated genes. However, most researchers have focused on single networks of single omics, which have less predictive power. With the available multiomics data, multilayer ne...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9861397/ https://www.ncbi.nlm.nih.gov/pubmed/36676027 http://dx.doi.org/10.3390/life13010076 |
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author | Xu, Xin-Jian Gao, Hong-Xiang Zhu, Liu-Cun Zhu, Rui |
author_facet | Xu, Xin-Jian Gao, Hong-Xiang Zhu, Liu-Cun Zhu, Rui |
author_sort | Xu, Xin-Jian |
collection | PubMed |
description | Network theory has attracted much attention from the biological community because of its high efficacy in identifying tumor-associated genes. However, most researchers have focused on single networks of single omics, which have less predictive power. With the available multiomics data, multilayer networks can now be used in molecular research. In this study, we achieved this with the construction of a bilayer network of DNA methylation sites and RNAs. We applied the network model to five types of tumor data to identify key genes associated with tumors. Compared with the single network, the proposed bilayer network resulted in more tumor-associated DNA methylation sites and genes, which we verified with prognostic and KEGG enrichment analyses. |
format | Online Article Text |
id | pubmed-9861397 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-98613972023-01-22 Identifying Tumor-Associated Genes from Bilayer Networks of DNA Methylation Sites and RNAs Xu, Xin-Jian Gao, Hong-Xiang Zhu, Liu-Cun Zhu, Rui Life (Basel) Article Network theory has attracted much attention from the biological community because of its high efficacy in identifying tumor-associated genes. However, most researchers have focused on single networks of single omics, which have less predictive power. With the available multiomics data, multilayer networks can now be used in molecular research. In this study, we achieved this with the construction of a bilayer network of DNA methylation sites and RNAs. We applied the network model to five types of tumor data to identify key genes associated with tumors. Compared with the single network, the proposed bilayer network resulted in more tumor-associated DNA methylation sites and genes, which we verified with prognostic and KEGG enrichment analyses. MDPI 2022-12-27 /pmc/articles/PMC9861397/ /pubmed/36676027 http://dx.doi.org/10.3390/life13010076 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Xu, Xin-Jian Gao, Hong-Xiang Zhu, Liu-Cun Zhu, Rui Identifying Tumor-Associated Genes from Bilayer Networks of DNA Methylation Sites and RNAs |
title | Identifying Tumor-Associated Genes from Bilayer Networks of DNA Methylation Sites and RNAs |
title_full | Identifying Tumor-Associated Genes from Bilayer Networks of DNA Methylation Sites and RNAs |
title_fullStr | Identifying Tumor-Associated Genes from Bilayer Networks of DNA Methylation Sites and RNAs |
title_full_unstemmed | Identifying Tumor-Associated Genes from Bilayer Networks of DNA Methylation Sites and RNAs |
title_short | Identifying Tumor-Associated Genes from Bilayer Networks of DNA Methylation Sites and RNAs |
title_sort | identifying tumor-associated genes from bilayer networks of dna methylation sites and rnas |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9861397/ https://www.ncbi.nlm.nih.gov/pubmed/36676027 http://dx.doi.org/10.3390/life13010076 |
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