A Bayesian Convolutional Neural Network Model with Uncertainty for Multi-label Text Classification on Mechanisms of Action (MoA) Prediction

Abstract

With the development of scientific research techniques, drug discovery has shifted from the serendipitous approach of the past to more targeted models based on an understanding of the underlying biological mechanisms of disease. However, there are hundreds or more of mechanism of action (MoA) data in the known drugs, which makes this process faced with complicated multi-label classification of text data. Traditional multi-label text classification algorithms will increase the complexity of the model and reduce the accuracy as the number of labels increases. Although deep learning algorithms can solve the problem of model complexity, they are currently only suitable for processing image format data. To overcome these problems, this study proposes a multi-label classification method based on Bayesian deep learning, which can convert non-image data format into image data, making it suitable for Convolutional neural network algorithm requirements. Then in the PyTorch environment, the Bayesian deep learning algorithm and the EfficientNet convolutional neural network are perfectly combined using the BLiTZ library to construct the Bayesian convolutional neural network model which named BCNNM. Not only improves the classification efficiency, this method also solves the problem of imbalanced classification of multi-label data, and fully considers the uncertainty in the neural network. In the process of drug development, this method has important practical significance for processing the multi-label classification of MoA data.