Discovering microbe-disease associations from the literature using a hierarchical long short-term memory network and an ensemble parser model
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Park, Yesol | - |
dc.contributor.author | Lee, Joohong | - |
dc.contributor.author | Moon, Heesang | - |
dc.contributor.author | Choi, Yong Suk | - |
dc.contributor.author | Rho, Mina | - |
dc.date.accessioned | 2022-07-07T00:55:18Z | - |
dc.date.available | 2022-07-07T00:55:18Z | - |
dc.date.created | 2021-07-14 | - |
dc.date.issued | 2021-02 | - |
dc.identifier.issn | 2045-2322 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/142311 | - |
dc.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. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | NATURE RESEARCH | - |
dc.title | Discovering microbe-disease associations from the literature using a hierarchical long short-term memory network and an ensemble parser model | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Choi, Yong Suk | - |
dc.contributor.affiliatedAuthor | Rho, Mina | - |
dc.identifier.doi | 10.1038/s41598-021-83966-8 | - |
dc.identifier.scopusid | 2-s2.0-85101548867 | - |
dc.identifier.wosid | 000626812400007 | - |
dc.identifier.bibliographicCitation | SCIENTIFIC REPORTS, v.11, no.1, pp.1 - 12 | - |
dc.relation.isPartOf | SCIENTIFIC REPORTS | - |
dc.citation.title | SCIENTIFIC REPORTS | - |
dc.citation.volume | 11 | - |
dc.citation.number | 1 | - |
dc.citation.startPage | 1 | - |
dc.citation.endPage | 12 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | Y | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Science & Technology - Other Topics | - |
dc.relation.journalWebOfScienceCategory | Multidisciplinary Sciences | - |
dc.subject.keywordPlus | DRUG INTERACTION EXTRACTION | - |
dc.subject.keywordPlus | TOOL | - |
dc.subject.keywordPlus | NORMALIZATION | - |
dc.subject.keywordPlus | RECOGNITION | - |
dc.subject.keywordPlus | INFECTIONS | - |
dc.subject.keywordPlus | DATABASE | - |
dc.subject.keywordPlus | PROTEIN | - |
dc.subject.keywordPlus | CORPUS | - |
dc.identifier.url | https://www.nature.com/articles/s41598-021-83966-8 | - |
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