A multi-view approach for predicting microbedisease associations by fusing the linear and nonlinear features

Microbes play a crucial role in human health and disease. Understanding the relationship between microbes and diseases is conducive to the treatment and diagnosis of diseases. Recently, many computational methods have been proposed to predict disease-microbe associations. However, most of the existing methods only consider a single model and explore the disease-microbe associations from a single view. To improve the prediction accuracy, we propose a novel multi-view approach that fuses the linear and nonlinear features to predict new potential associations between diseases and microbes. We first design a non-negative matrix tri-factorization method to extract the linear features of diseases and microbes. We input the linear features from the non-negative matrix tri-factorization model and bi-random walk model into a capsule neural network to obtain the diseases and microbes’ nonlinear features. Finally, we leverage a logistic regression model to combine the non-negative matrix tri-factorization model predictions, bi-random walk model predictions and the capsule neural network predictions to obtain the final association scores between microbes and diseases. We apply our method to predict human microbedisease associations. Experimental results show that our fusion model outperforms the non-negative matrix tri-factorization model, bi-random walk model and other existing models.