Discovering microbe-disease associations from the literature using a hierarchical long short-term memory network and an ensemble parser model
- Title
- Discovering microbe-disease associations from the literature using a hierarchical long short-term memory network and an ensemble parser model
- Author
- 노미나
- Issue Date
- 2021-02
- Publisher
- NATURE RESEARCH
- Citation
- SCIENTIFIC REPORTS, v. 11, no. 1, article no. 4490, Page. 1-12
- 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.
- URI
- https://www.nature.com/articles/s41598-021-83966-8https://repository.hanyang.ac.kr/handle/20.500.11754/175658
- ISSN
- 2045-2322
- DOI
- 10.1038/s41598-021-83966-8
- Appears in Collections:
- COLLEGE OF ENGINEERING[S](공과대학) > COMPUTER SCIENCE(컴퓨터소프트웨어학부) > Articles
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