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Graph Embedding Based Novel Gene Discovery Associated With Diabetes Mellitus
Diabetes mellitus is a group of complex metabolic disorders which has affected hundreds of millions of patients world-widely. The underlying pathogenesis of various types of diabetes is still unclear, which hinders the way of developing more efficient therapies. Although many genes have been found a...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8657768/ https://www.ncbi.nlm.nih.gov/pubmed/34899863 http://dx.doi.org/10.3389/fgene.2021.779186 |
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author | Du, Jianzong Lin, Dongdong Yuan, Ruan Chen, Xiaopei Liu, Xiaoli Yan, Jing |
author_facet | Du, Jianzong Lin, Dongdong Yuan, Ruan Chen, Xiaopei Liu, Xiaoli Yan, Jing |
author_sort | Du, Jianzong |
collection | PubMed |
description | Diabetes mellitus is a group of complex metabolic disorders which has affected hundreds of millions of patients world-widely. The underlying pathogenesis of various types of diabetes is still unclear, which hinders the way of developing more efficient therapies. Although many genes have been found associated with diabetes mellitus, more novel genes are still needed to be discovered towards a complete picture of the underlying mechanism. With the development of complex molecular networks, network-based disease-gene prediction methods have been widely proposed. However, most existing methods are based on the hypothesis of guilt-by-association and often handcraft node features based on local topological structures. Advances in graph embedding techniques have enabled automatically global feature extraction from molecular networks. Inspired by the successful applications of cutting-edge graph embedding methods on complex diseases, we proposed a computational framework to investigate novel genes associated with diabetes mellitus. There are three main steps in the framework: network feature extraction based on graph embedding methods; feature denoising and regeneration using stacked autoencoder; and disease-gene prediction based on machine learning classifiers. We compared the performance by using different graph embedding methods and machine learning classifiers and designed the best workflow for predicting genes associated with diabetes mellitus. Functional enrichment analysis based on Human Phenotype Ontology (HPO), KEGG, and GO biological process and publication search further evaluated the predicted novel genes. |
format | Online Article Text |
id | pubmed-8657768 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-86577682021-12-10 Graph Embedding Based Novel Gene Discovery Associated With Diabetes Mellitus Du, Jianzong Lin, Dongdong Yuan, Ruan Chen, Xiaopei Liu, Xiaoli Yan, Jing Front Genet Genetics Diabetes mellitus is a group of complex metabolic disorders which has affected hundreds of millions of patients world-widely. The underlying pathogenesis of various types of diabetes is still unclear, which hinders the way of developing more efficient therapies. Although many genes have been found associated with diabetes mellitus, more novel genes are still needed to be discovered towards a complete picture of the underlying mechanism. With the development of complex molecular networks, network-based disease-gene prediction methods have been widely proposed. However, most existing methods are based on the hypothesis of guilt-by-association and often handcraft node features based on local topological structures. Advances in graph embedding techniques have enabled automatically global feature extraction from molecular networks. Inspired by the successful applications of cutting-edge graph embedding methods on complex diseases, we proposed a computational framework to investigate novel genes associated with diabetes mellitus. There are three main steps in the framework: network feature extraction based on graph embedding methods; feature denoising and regeneration using stacked autoencoder; and disease-gene prediction based on machine learning classifiers. We compared the performance by using different graph embedding methods and machine learning classifiers and designed the best workflow for predicting genes associated with diabetes mellitus. Functional enrichment analysis based on Human Phenotype Ontology (HPO), KEGG, and GO biological process and publication search further evaluated the predicted novel genes. Frontiers Media S.A. 2021-11-25 /pmc/articles/PMC8657768/ /pubmed/34899863 http://dx.doi.org/10.3389/fgene.2021.779186 Text en Copyright © 2021 Du, Lin, Yuan, Chen, Liu and Yan. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Genetics Du, Jianzong Lin, Dongdong Yuan, Ruan Chen, Xiaopei Liu, Xiaoli Yan, Jing Graph Embedding Based Novel Gene Discovery Associated With Diabetes Mellitus |
title | Graph Embedding Based Novel Gene Discovery Associated With Diabetes Mellitus |
title_full | Graph Embedding Based Novel Gene Discovery Associated With Diabetes Mellitus |
title_fullStr | Graph Embedding Based Novel Gene Discovery Associated With Diabetes Mellitus |
title_full_unstemmed | Graph Embedding Based Novel Gene Discovery Associated With Diabetes Mellitus |
title_short | Graph Embedding Based Novel Gene Discovery Associated With Diabetes Mellitus |
title_sort | graph embedding based novel gene discovery associated with diabetes mellitus |
topic | Genetics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8657768/ https://www.ncbi.nlm.nih.gov/pubmed/34899863 http://dx.doi.org/10.3389/fgene.2021.779186 |
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