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10.1186-s12859-021-04346-7 |
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|a 14712105 (ISSN)
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|a Mining microbe–disease interactions from literature via a transfer learning model
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|b BioMed Central Ltd
|c 2021
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|z View Fulltext in Publisher
|u https://doi.org/10.1186/s12859-021-04346-7
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|a 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 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. Results: 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. Conclusions: Evaluation results demonstrate that our method outperform the baseline model (rule-based PKDE4J) with an average F1-score of 73.81%. For further validation, we randomly sampled nearly 1000 predicted interactions by our model, and manually checked the correctness of each interaction, which gives a 73% accuracy. The MDIDB webiste is freely avaliable throuth http://dbmdi.com/index/. © 2021, The Author(s).
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|a Bacteria
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|a Biomedical literature
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|a Biomedical research
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|a Biomedical Research
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|a data mining
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|a Data Mining
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|a Deep learning
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|a Detection performance
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|a Drug interactions
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|a Evaluation results
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|a Interaction extraction
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|a Large-scale database
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|a Learning systems
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|a machine learning
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|a Machine Learning
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|a medical research
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|a Microbe–disease interactions
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|a Named entity recognition
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|a Named-entity recognition
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|a Natural language processing systems
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|a publication
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|a Publications
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|a Query processing
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|a Relation extraction
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|a Structured database
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|a Text mining
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|a Transfer learning
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|a Transfer learning
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|a User interfaces
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|a Chen, J.X.
|e author
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|a Qiu, Y.
|e author
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|a Wu, C.
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|a Xiao, X.
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|a Yang, C.
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|a Yi, J.
|e author
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|t BMC Bioinformatics
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