Mining microbe–disease interactions from literature via a transfer learning model

Interactions of microbes and diseases are of great importance for biomedical research. However, large-scale of microbe-disease interactions are hidden in the biomedical literature. The structured databases for microbe-disease interactions are in limited amounts. In this paper, we aim to construct a...

Full description

Saved in:
Bibliographic Details
Published in:BMC bioinformatics 2021-09, Vol.22 (1), p.1-15, Article 432
Main Authors: Wu, Chengkun, Xiao, Xinyi, Yang, Canqun, Chen, JinXiang, Yi, Jiacai, Qiu, Yanlong
Format: Article
Language:English
Subjects:
Associations
Automation
Browsing
Data mining
Data search
Deep learning
Dictionaries
Disease
Error reduction
Health aspects
Machine learning
Medical research
Methods
Microbe–disease interactions
Microbiota
Microorganisms
Named-entity recognition
Natural language
Pathogenic microorganisms
Performance evaluation
Relation extraction
Silver mines
Taxonomy
Therapeutic targets
Transfer learning
Websites
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Interactions of microbes and diseases are of great importance for biomedical research. However, large-scale of microbe-disease interactions are hidden in the biomedical literature. The structured databases for microbe-disease interactions are in limited amounts. In this paper, we aim to construct a large-scale database for microbe-disease interactions automatically. We attained this goal via applying text mining methods based on a deep learning model with a moderate curation cost. We also built a user-friendly web interface that allows researchers to navigate and query required information. Firstly, we manually constructed a golden-standard corpus and a sliver-standard corpus (SSC) for microbe-disease interactions for curation. Moreover, we proposed a text mining framework for microbe-disease interaction extraction based on a pretrained model BERE. We applied named entity recognition tools to detect microbe and disease mentions from the free biomedical texts. After that, we fine-tuned the pretrained model BERE to recognize relations between targeted entities, which was originally built for drug-target interactions or drug-drug interactions. The introduction of SSC for model fine-tuning greatly improved detection performance for microbe-disease interactions, with an average reduction in error of approximately 10%. The MDIDB website offers data browsing, custom searching for specific diseases or microbes, and batch downloading.
ISSN:1471-2105
1471-2105
DOI:10.1186/s12859-021-04346-7