Microbe Drug Association Prediction with Bernoulli Random Forests

Due to the widespread use of antibiotics, many microbes have become drug-resistant. It is urgent to develop new antibiotics that can effectively combat drug-resistant microbes. Exploiting microbe-drug 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-drug association (BRFMDA) prediction. First, we introduced integrated drug similarity and integrated microbe similarity to construct feature of each microbe-drug pair. Second, based on known microbe-drug 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-drug 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-drug 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-drug 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 ± 0.0112 in fivefold cross-validation. On the abiofilm dataset, BRFMDA achieved an AUC of 0.9130 in global LOOCV, 0.8927 in local LOOCV, and 0.8844 ± 0.0137 in fivefold cross-validation.