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)

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...