Discovering microbe-disease associations from the literature using a hierarchical long short-term memory network and an ensemble parser model

Abstract With recent advances in biotechnology and sequencing technology, the microbial community has been intensively studied and discovered to be associated with many chronic as well as acute diseases. Even though a tremendous number of studies describing the association between microbes and disea...

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Autores principales:	Yesol Park, Joohong Lee, Heesang Moon, Yong Suk Choi, Mina Rho
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/0f472126253a466bb775f377a1440755
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spelling	oai:doaj.org-article:0f472126253a466bb775f377a14407552021-12-02T13:30:12ZDiscovering microbe-disease associations from the literature using a hierarchical long short-term memory network and an ensemble parser model10.1038/s41598-021-83966-82045-2322https://doaj.org/article/0f472126253a466bb775f377a14407552021-02-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-83966-8https://doaj.org/toc/2045-2322Abstract With recent advances in biotechnology and sequencing technology, the microbial community has been intensively studied and discovered to be associated with many chronic as well as acute diseases. Even though a tremendous number of studies describing the association between microbes and diseases have been published, text mining methods that focus on such associations have been rarely studied. We propose a framework that combines machine learning and natural language processing methods to analyze the association between microbes and diseases. A hierarchical long short-term memory network was used to detect sentences that describe the association. For the sentences determined, two different parse tree-based search methods were combined to find the relation-describing word. The ensemble model of constituency parsing for structural pattern matching and dependency-based relation extraction improved the prediction accuracy. By combining deep learning and parse tree-based extractions, our proposed framework could extract the microbe-disease association with higher accuracy. The evaluation results showed that our system achieved an F-score of 0.8764 and 0.8524 in binary decisions and extracting relation words, respectively. As a case study, we performed a large-scale analysis of the association between microbes and diseases. Additionally, a set of common microbes shared by multiple diseases were also identified in this study. This study could provide valuable information for the major microbes that were studied for a specific disease. The code and data are available at https://github.com/DMnBI/mdi_predictor .Yesol ParkJoohong LeeHeesang MoonYong Suk ChoiMina RhoNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Yesol Park Joohong Lee Heesang Moon Yong Suk Choi Mina Rho Discovering microbe-disease associations from the literature using a hierarchical long short-term memory network and an ensemble parser model
description	Abstract With recent advances in biotechnology and sequencing technology, the microbial community has been intensively studied and discovered to be associated with many chronic as well as acute diseases. Even though a tremendous number of studies describing the association between microbes and diseases have been published, text mining methods that focus on such associations have been rarely studied. We propose a framework that combines machine learning and natural language processing methods to analyze the association between microbes and diseases. A hierarchical long short-term memory network was used to detect sentences that describe the association. For the sentences determined, two different parse tree-based search methods were combined to find the relation-describing word. The ensemble model of constituency parsing for structural pattern matching and dependency-based relation extraction improved the prediction accuracy. By combining deep learning and parse tree-based extractions, our proposed framework could extract the microbe-disease association with higher accuracy. The evaluation results showed that our system achieved an F-score of 0.8764 and 0.8524 in binary decisions and extracting relation words, respectively. As a case study, we performed a large-scale analysis of the association between microbes and diseases. Additionally, a set of common microbes shared by multiple diseases were also identified in this study. This study could provide valuable information for the major microbes that were studied for a specific disease. The code and data are available at https://github.com/DMnBI/mdi_predictor .
format	article
author	Yesol Park Joohong Lee Heesang Moon Yong Suk Choi Mina Rho
author_facet	Yesol Park Joohong Lee Heesang Moon Yong Suk Choi Mina Rho
author_sort	Yesol Park
title	Discovering microbe-disease associations from the literature using a hierarchical long short-term memory network and an ensemble parser model
title_short	Discovering microbe-disease associations from the literature using a hierarchical long short-term memory network and an ensemble parser model
title_full	Discovering microbe-disease associations from the literature using a hierarchical long short-term memory network and an ensemble parser model
title_fullStr	Discovering microbe-disease associations from the literature using a hierarchical long short-term memory network and an ensemble parser model
title_full_unstemmed	Discovering microbe-disease associations from the literature using a hierarchical long short-term memory network and an ensemble parser model
title_sort	discovering microbe-disease associations from the literature using a hierarchical long short-term memory network and an ensemble parser model
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/0f472126253a466bb775f377a1440755
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Discovering microbe-disease associations from the literature using a hierarchical long short-term memory network and an ensemble parser model

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