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However, large-scale of microbe\u2013disease interactions are hidden in the biomedical literature. The structured databases for microbe\u2013disease interactions are in limited amounts. In this paper, we aim to construct a large-scale database for microbe\u2013disease 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.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>Firstly, we manually constructed a golden-standard corpus and a sliver-standard corpus (SSC) for microbe\u2013disease interactions for curation. Moreover, we proposed a text mining framework for microbe\u2013disease 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\u2013target interactions or drug\u2013drug interactions. The introduction of SSC for model fine-tuning greatly improved detection performance for microbe\u2013disease 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.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusions<\/jats:title>\n                <jats:p>Evaluation results demonstrate that our method outperform the baseline model (rule-based PKDE4J) with an average <jats:inline-formula><jats:alternatives><jats:tex-math>$$F_1$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                    <mml:msub>\n                      <mml:mi>F<\/mml:mi>\n                      <mml:mn>1<\/mml:mn>\n                    <\/mml:msub>\n                  <\/mml:math><\/jats:alternatives><\/jats:inline-formula>-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. 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