Prediction of microbe-drug associations using a CNN-Bernoulli random forest model
Abstract
Background: Antibiotics play a critical role in treating microbial infections. However, their widespread use has contributed to the growing problem of microbial resistance. Addressing this challenge requires the identification of new microbe-drug associations to support the development of novel therapeutic strategies. Since traditional wet-lab experiments are time-consuming and costly, computational models offer an efficient alternative for discovering potential applications of existing drugs against previously untested microbes. These models can facilitate the identification of novel microbe-drug associations and help counteract resistance mechanisms. Methods: This study proposes a novel computational model: convolutional neural network with Bernoulli random forest for Microbe-Drug Association prediction (CNNBRFMDA). The model constructs feature vectors for all microbe-drug pairs based on known associations, microbe similarity, and drug similarity. A subset of these vectors is randomly selected to form the training set. A convolutional neural network (CNN) is then used to reduce the dimensionality of all feature vectors, including those in the training set. The reduced training set is subsequently used to train a Bernoulli random forest (BRF) to predict potential microbe-drug associations. The innovation of CNNBRFMDA lies in its integration of CNN for nonlinear feature extraction and BRF for robust prediction. This approach enhances computational efficiency and improves the…
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