Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 최용석 | - |
dc.date.accessioned | 2022-10-21T01:34:16Z | - |
dc.date.available | 2022-10-21T01:34:16Z | - |
dc.date.issued | 2021-02 | - |
dc.identifier.citation | SCIENTIFIC REPORTS, v. 11, NO 1, article no. 4490 | en_US |
dc.identifier.issn | 2045-2322 | en_US |
dc.identifier.uri | https://www.nature.com/articles/s41598-021-83966-8 | en_US |
dc.identifier.uri | https://repository.hanyang.ac.kr/handle/20.500.11754/175659 | - |
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. | en_US |
dc.description.sponsorship | This work was supported by Bio & Medical Technology Development Program of the National Research Foundation of Korea (NRF), funded by the Ministry of Science, ICT & Future Planning (2017M3A9F3041232), Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea Government (MSIT) [No. 2020-0-01373, Artificial Intelligence Graduate School Program (Hanyang University)], and the Collaborative Genome Program of the Korea Institute of Marine Science and Technology Promotion (KIMST), funded by the Ministry of Oceans and Fisheries (MOF) [No. 20180430]. | en_US |
dc.language.iso | en | en_US |
dc.publisher | NATURE RESEARCH | en_US |
dc.title | Discovering microbe-disease associations from the literature using a hierarchical long short-term memory network and an ensemble parser model | en_US |
dc.type | Article | en_US |
dc.relation.no | 1 | - |
dc.relation.volume | 11 | - |
dc.identifier.doi | 10.1038/s41598-021-83966-8 | en_US |
dc.relation.journal | SCIENTIFIC REPORTS | - |
dc.contributor.googleauthor | Park, Yesol | - |
dc.contributor.googleauthor | Lee, Joohong | - |
dc.contributor.googleauthor | Moon, Heesang | - |
dc.contributor.googleauthor | Choi, Yong Suk | - |
dc.contributor.googleauthor | Rho, Mina | - |
dc.relation.code | 2021002638 | - |
dc.sector.campus | S | - |
dc.sector.daehak | COLLEGE OF ENGINEERING[S] | - |
dc.sector.department | SCHOOL OF COMPUTER SCIENCE | - |
dc.identifier.pid | cys | - |
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