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)
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-021-83966-8

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 .