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Gene based message passing for drug repurposing

The medicinal effect of a drug acts through a series of genes, and the pathological mechanism of a disease is also related to genes with certain biological functions. However, the complex information between drug or disease and a series of genes is neglected by traditional message passing methods. I...

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Detalles Bibliográficos
Autores principales: Wang, Yuxing, Li, Zhiyang, Rao, Jiahua, Yang, Yuedong, Dai, Zhiming
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10475505/
https://www.ncbi.nlm.nih.gov/pubmed/37670781
http://dx.doi.org/10.1016/j.isci.2023.107663
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author Wang, Yuxing
Li, Zhiyang
Rao, Jiahua
Yang, Yuedong
Dai, Zhiming
author_facet Wang, Yuxing
Li, Zhiyang
Rao, Jiahua
Yang, Yuedong
Dai, Zhiming
author_sort Wang, Yuxing
collection PubMed
description The medicinal effect of a drug acts through a series of genes, and the pathological mechanism of a disease is also related to genes with certain biological functions. However, the complex information between drug or disease and a series of genes is neglected by traditional message passing methods. In this study, we proposed a new framework using two different strategies for gene-drug/disease and drug-disease networks, respectively. We employ long short-term memory (LSTM) network to extract the flow of message from series of genes (gene path) to drug/disease. Incorporating the resulting information of gene paths into drug-disease network, we utilize graph convolutional network (GCN) to predict drug-disease associations. Experimental results showed that our method GeneDR (gene-based drug repurposing) makes better use of the information in gene paths, and performs better in predicting drug-disease associations.
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spelling pubmed-104755052023-09-05 Gene based message passing for drug repurposing Wang, Yuxing Li, Zhiyang Rao, Jiahua Yang, Yuedong Dai, Zhiming iScience Article The medicinal effect of a drug acts through a series of genes, and the pathological mechanism of a disease is also related to genes with certain biological functions. However, the complex information between drug or disease and a series of genes is neglected by traditional message passing methods. In this study, we proposed a new framework using two different strategies for gene-drug/disease and drug-disease networks, respectively. We employ long short-term memory (LSTM) network to extract the flow of message from series of genes (gene path) to drug/disease. Incorporating the resulting information of gene paths into drug-disease network, we utilize graph convolutional network (GCN) to predict drug-disease associations. Experimental results showed that our method GeneDR (gene-based drug repurposing) makes better use of the information in gene paths, and performs better in predicting drug-disease associations. Elsevier 2023-08-18 /pmc/articles/PMC10475505/ /pubmed/37670781 http://dx.doi.org/10.1016/j.isci.2023.107663 Text en © 2023 The Authors https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Wang, Yuxing
Li, Zhiyang
Rao, Jiahua
Yang, Yuedong
Dai, Zhiming
Gene based message passing for drug repurposing
title Gene based message passing for drug repurposing
title_full Gene based message passing for drug repurposing
title_fullStr Gene based message passing for drug repurposing
title_full_unstemmed Gene based message passing for drug repurposing
title_short Gene based message passing for drug repurposing
title_sort gene based message passing for drug repurposing
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10475505/
https://www.ncbi.nlm.nih.gov/pubmed/37670781
http://dx.doi.org/10.1016/j.isci.2023.107663
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