Microbe Drug Association Prediction with Bernoulli Random Forests

Abstract

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.