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Relation path feature embedding based convolutional neural network method for drug discovery
BACKGROUND: Drug development is an expensive and time-consuming process. Literature-based discovery has played a critical role in drug development and may be a supplementary method to help scientists speed up the discovery of drugs. METHODS: Here, we propose a relation path features embedding based...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6454669/ https://www.ncbi.nlm.nih.gov/pubmed/30961599 http://dx.doi.org/10.1186/s12911-019-0764-5 |
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author | Zhao, Di Wang, Jian Sang, Shengtian Lin, Hongfei Wen, Jiabin Yang, Chunmei |
author_facet | Zhao, Di Wang, Jian Sang, Shengtian Lin, Hongfei Wen, Jiabin Yang, Chunmei |
author_sort | Zhao, Di |
collection | PubMed |
description | BACKGROUND: Drug development is an expensive and time-consuming process. Literature-based discovery has played a critical role in drug development and may be a supplementary method to help scientists speed up the discovery of drugs. METHODS: Here, we propose a relation path features embedding based convolutional neural network model with attention mechanism for drug discovery from literature, which we denote as PACNN. First, we use predications from biomedical abstracts to construct a biomedical knowledge graph, and then apply a path ranking algorithm to extract drug-disease relation path features on the biomedical knowledge graph. After that, we use these drug-disease relation features to train a convolutional neural network model which combined with the attention mechanism. Finally, we employ the trained models to mine drugs for treating diseases. RESULTS: The experiment shows that the proposed model achieved promising results, comparing to several random walk algorithms. CONCLUSIONS: In this paper, we propose a relation path features embedding based convolutional neural network with attention mechanism for discovering potential drugs from literature. Our method could be an auxiliary method for drug discovery, which can speed up the discovery of new drugs for the incurable diseases. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12911-019-0764-5) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-6454669 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-64546692019-04-19 Relation path feature embedding based convolutional neural network method for drug discovery Zhao, Di Wang, Jian Sang, Shengtian Lin, Hongfei Wen, Jiabin Yang, Chunmei BMC Med Inform Decis Mak Research BACKGROUND: Drug development is an expensive and time-consuming process. Literature-based discovery has played a critical role in drug development and may be a supplementary method to help scientists speed up the discovery of drugs. METHODS: Here, we propose a relation path features embedding based convolutional neural network model with attention mechanism for drug discovery from literature, which we denote as PACNN. First, we use predications from biomedical abstracts to construct a biomedical knowledge graph, and then apply a path ranking algorithm to extract drug-disease relation path features on the biomedical knowledge graph. After that, we use these drug-disease relation features to train a convolutional neural network model which combined with the attention mechanism. Finally, we employ the trained models to mine drugs for treating diseases. RESULTS: The experiment shows that the proposed model achieved promising results, comparing to several random walk algorithms. CONCLUSIONS: In this paper, we propose a relation path features embedding based convolutional neural network with attention mechanism for discovering potential drugs from literature. Our method could be an auxiliary method for drug discovery, which can speed up the discovery of new drugs for the incurable diseases. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12911-019-0764-5) contains supplementary material, which is available to authorized users. BioMed Central 2019-04-09 /pmc/articles/PMC6454669/ /pubmed/30961599 http://dx.doi.org/10.1186/s12911-019-0764-5 Text en © The Author(s) 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Zhao, Di Wang, Jian Sang, Shengtian Lin, Hongfei Wen, Jiabin Yang, Chunmei Relation path feature embedding based convolutional neural network method for drug discovery |
title | Relation path feature embedding based convolutional neural network method for drug discovery |
title_full | Relation path feature embedding based convolutional neural network method for drug discovery |
title_fullStr | Relation path feature embedding based convolutional neural network method for drug discovery |
title_full_unstemmed | Relation path feature embedding based convolutional neural network method for drug discovery |
title_short | Relation path feature embedding based convolutional neural network method for drug discovery |
title_sort | relation path feature embedding based convolutional neural network method for drug discovery |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6454669/ https://www.ncbi.nlm.nih.gov/pubmed/30961599 http://dx.doi.org/10.1186/s12911-019-0764-5 |
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