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

• Published in: Scientific reports Vol. 11; no. 1; p. 4490
• Main Authors: Park, Yesol, Lee, Joohong, Moon, Heesang, Choi, Yong Suk, Rho, Mina
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 24.02.2021; Nature Publishing Group; Nature Portfolio
• Subjects: 631/114; 631/114/1305; 631/114/2164; 631/114/2406; 631/326; Biotechnology; Data mining; Data Mining - methods; Deep learning; Disease; Humanities and Social Sciences; Humans; Language; Learning algorithms; Long short-term memory; Machine Learning; Memory, Short-Term - physiology; Microbiota - physiology; Microorganisms; multidisciplinary; Natural Language Processing; Science; Science (multidisciplinary)
• DOI: 10.1038/s41598-021-83966-8
• ISSN: 2045-2322

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 .