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It is urgent to develop new antibiotics that can effectively combat drug-resistant microbes. Exploiting microbe\u2013drug associations can help researchers make progress in drug development. In this paper, we develop for the first time a computational model of Bernoulli random forest (BRF) for microbe\u2013drug association (BRFMDA) prediction. First, we introduced integrated drug similarity and integrated microbe similarity to construct feature of each microbe\u2013drug pair. Second, based on known microbe\u2013drug association, we obtained the features of all positive sample. Then, the same number of negative samples as the number of positive samples were chosen from unknown microbe\u2013drug pairs. Next, we used a filter-based approach to reduce the dimension of features of positive and negative samples. Lastly, BRF was used to train features of positive and negative samples to predict microbe\u2013drug associations. For validating the performance of BRFMDA, we took leave-one-out cross-validation (LOOCV) and fivefold cross-validation, as well as two types of case studies, to validate the prediction performance of BRFMDA. The results of cross-validation and case studies suggested that BRFMDA is a dependable model for predicting potential microbe\u2013drug associations. Specifically, on the Microbe-Drug Association Database (MDAD), BRFMDA obtained an area under the curve (AUC) of 0.9134 in global LOOCV, 0.8958 in local LOOCV, and 0.8657 \u00b1 0.0112 in fivefold cross-validation. 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