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...

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Autores principales:	Jianzong Du, Dongdong Lin, Ruan Yuan, Xiaopei Chen, Xiaoli Liu, Jing Yan
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	diabetes mellitus graph embedding novel gene discovery molecular network disease gene prediction Genetics QH426-470
Acceso en línea:	https://doaj.org/article/c1d0cf5952a84b24a761cb68e23a744a
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spelling	oai:doaj.org-article:c1d0cf5952a84b24a761cb68e23a744a2021-12-01T03:33:11ZGraph Embedding Based Novel Gene Discovery Associated With Diabetes Mellitus1664-802110.3389/fgene.2021.779186https://doaj.org/article/c1d0cf5952a84b24a761cb68e23a744a2021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fgene.2021.779186/fullhttps://doaj.org/toc/1664-8021Diabetes 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.Jianzong DuDongdong LinRuan YuanXiaopei ChenXiaoli LiuJing YanJing YanFrontiers Media S.A.articlediabetes mellitusgraph embeddingnovel gene discoverymolecular networkdisease gene predictionGeneticsQH426-470ENFrontiers in Genetics, Vol 12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	diabetes mellitus graph embedding novel gene discovery molecular network disease gene prediction Genetics QH426-470
spellingShingle	diabetes mellitus graph embedding novel gene discovery molecular network disease gene prediction Genetics QH426-470 Jianzong Du Dongdong Lin Ruan Yuan Xiaopei Chen Xiaoli Liu Jing Yan Jing Yan Graph Embedding Based Novel Gene Discovery Associated With Diabetes Mellitus
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	article
author	Jianzong Du Dongdong Lin Ruan Yuan Xiaopei Chen Xiaoli Liu Jing Yan Jing Yan
author_facet	Jianzong Du Dongdong Lin Ruan Yuan Xiaopei Chen Xiaoli Liu Jing Yan Jing Yan
author_sort	Jianzong Du
title	Graph Embedding Based Novel Gene Discovery Associated With Diabetes Mellitus
title_short	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_sort	graph embedding based novel gene discovery associated with diabetes mellitus
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/c1d0cf5952a84b24a761cb68e23a744a
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_version_	1718405924930977792

Graph Embedding Based Novel Gene Discovery Associated With Diabetes Mellitus

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