Integrating Deep Textual Features To Probability Matrix Factorization For Metabolite-Disease Association Prediction

Metabolic disorders play an important role in the development of many common diseases, including obesity, diabetes and coronary heart disease. Identifying key metabolites associated with disease can help us understand the mechanism of disease better and improve clinical diagnosis. Predicting diseases-related metabolites through computational approaches can provide potential biomarkers for further biological experiments. Text annotations on metabolites in existing databases provide prior information, which could provide more information about metabolites. However, current approaches haven't taken this information into consideration. In this work, we proposed a probability matrix factorization method which combined deep textual features to predict metabolite-disease associations. The deep neural network combining convolutional neural network and gated recurrent unit network is used to extract the corresponding features from text annotations of metabolites and diseases. Then, associations between metabolites and diseases are predicted through the matrix factorization based on these textual features. The main contributions in the work is that our model shows that adding textual features could help to improve the prediction of metabolite-disease associations. Case studies have indicated our model got predictive ability for diseases-related metabolites.