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

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

Background: 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...

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Bibliographic Details
Main Authors: Chen, J.X (Author), Qiu, Y. (Author), Wu, C. (Author), Xiao, X. (Author), Yang, C. (Author), Yi, J. (Author)
Format: Article
Language:English
Published: BioMed Central Ltd 2021
Subjects:
Bacteria
Biomedical literature
Biomedical research
Biomedical Research
data mining
Data Mining
Deep learning
Detection performance
Drug interactions
Evaluation results
Interaction extraction
Large-scale database
Learning systems
machine learning
Machine Learning
medical research
Microbe–disease interactions
Named entity recognition
Named-entity recognition
Natural language processing systems
publication
Publications
Query processing
Relation extraction
Structured database
Text mining
Transfer learning
User interfaces
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