Identifying disease-gene associations using a convolutional neural network-based model by embedding a biological knowledge graph with entity descriptions.

Understanding the role of genes in human disease is of high importance. However, identifying genes associated with human diseases requires laborious experiments that involve considerable effort and time. Therefore, a computational approach to predict candidate genes related to complex diseases inclu...

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Autores principales:	Wonjun Choi, Hyunju Lee
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/6942b3709938425c8b9d4d00f5aa23a2
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spelling	oai:doaj.org-article:6942b3709938425c8b9d4d00f5aa23a22021-12-02T20:16:51ZIdentifying disease-gene associations using a convolutional neural network-based model by embedding a biological knowledge graph with entity descriptions.1932-620310.1371/journal.pone.0258626https://doaj.org/article/6942b3709938425c8b9d4d00f5aa23a22021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0258626https://doaj.org/toc/1932-6203Understanding the role of genes in human disease is of high importance. However, identifying genes associated with human diseases requires laborious experiments that involve considerable effort and time. Therefore, a computational approach to predict candidate genes related to complex diseases including cancer has been extensively studied. In this study, we propose a convolutional neural network-based knowledge graph-embedding model (KGED), which is based on a biological knowledge graph with entity descriptions to infer relationships between biological entities. As an application demonstration, we generated gene-interaction networks for each cancer type using gene-gene relationships inferred by KGED. We then analyzed the constructed gene networks using network centrality measures, including betweenness, closeness, degree, and eigenvector centrality metrics, to rank the central genes of the network and identify highly correlated cancer genes. Furthermore, we evaluated our proposed approach for prostate, breast, and lung cancers by comparing the performance with that of existing approaches. The KGED model showed improved performance in predicting cancer-related genes using the inferred gene-gene interactions. Thus, we conclude that gene-gene interactions inferred by KGED can be helpful for future research, such as that aimed at future research on pathogenic mechanisms of human diseases, and contribute to the field of disease treatment discovery.Wonjun ChoiHyunju LeePublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 10, p e0258626 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Wonjun Choi Hyunju Lee Identifying disease-gene associations using a convolutional neural network-based model by embedding a biological knowledge graph with entity descriptions.
description	Understanding the role of genes in human disease is of high importance. However, identifying genes associated with human diseases requires laborious experiments that involve considerable effort and time. Therefore, a computational approach to predict candidate genes related to complex diseases including cancer has been extensively studied. In this study, we propose a convolutional neural network-based knowledge graph-embedding model (KGED), which is based on a biological knowledge graph with entity descriptions to infer relationships between biological entities. As an application demonstration, we generated gene-interaction networks for each cancer type using gene-gene relationships inferred by KGED. We then analyzed the constructed gene networks using network centrality measures, including betweenness, closeness, degree, and eigenvector centrality metrics, to rank the central genes of the network and identify highly correlated cancer genes. Furthermore, we evaluated our proposed approach for prostate, breast, and lung cancers by comparing the performance with that of existing approaches. The KGED model showed improved performance in predicting cancer-related genes using the inferred gene-gene interactions. Thus, we conclude that gene-gene interactions inferred by KGED can be helpful for future research, such as that aimed at future research on pathogenic mechanisms of human diseases, and contribute to the field of disease treatment discovery.
format	article
author	Wonjun Choi Hyunju Lee
author_facet	Wonjun Choi Hyunju Lee
author_sort	Wonjun Choi
title	Identifying disease-gene associations using a convolutional neural network-based model by embedding a biological knowledge graph with entity descriptions.
title_short	Identifying disease-gene associations using a convolutional neural network-based model by embedding a biological knowledge graph with entity descriptions.
title_full	Identifying disease-gene associations using a convolutional neural network-based model by embedding a biological knowledge graph with entity descriptions.
title_fullStr	Identifying disease-gene associations using a convolutional neural network-based model by embedding a biological knowledge graph with entity descriptions.
title_full_unstemmed	Identifying disease-gene associations using a convolutional neural network-based model by embedding a biological knowledge graph with entity descriptions.
title_sort	identifying disease-gene associations using a convolutional neural network-based model by embedding a biological knowledge graph with entity descriptions.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/6942b3709938425c8b9d4d00f5aa23a2
work_keys_str_mv	AT wonjunchoi identifyingdiseasegeneassociationsusingaconvolutionalneuralnetworkbasedmodelbyembeddingabiologicalknowledgegraphwithentitydescriptions AT hyunjulee identifyingdiseasegeneassociationsusingaconvolutionalneuralnetworkbasedmodelbyembeddingabiologicalknowledgegraphwithentitydescriptions
_version_	1718374408795455488

Identifying disease-gene associations using a convolutional neural network-based model by embedding a biological knowledge graph with entity descriptions.

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