Integrating Deep Textual Features to Probability Matrix Factorization for Metabolitedisease 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 diseasesrelated 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 havenu0027t taken this information into consideration. In this work, we proposed a probability matrix factorization method which combined deep textual features to predict metabolitedisease 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 metabolitedisease associations. Case studies have indicated our model got predictive ability for diseasesrelated metabolites.

LncRNAxe2x80x93disease association prediction through combining linear and nonlinear features with matrix factorization and deep learning techniques Long noncoding RNAs (lncRNAs) are the foundation for understanding mechanisms of many human diseases. Considering the limited number of known experimentally verified associations between lncRNAs and diseases, it is appealing to develop accurate and effective computational methods to identify lncRNAdisease associations. Conventional matrix factorizationbased methods cannot model complicated associations between lncRNAs and diseases. In this study, we propose a novel computational framework, through combining linear and nonlinear features, which is used for lncRNAdisease association prediction. In our model, a conventional matrix factorization method is applied to extract linear features between lncRNAs and diseases. Deep learning techniques (fully connected layers) are applied to extract nonlinear features between lncRNAs and diseases. Finally, linear and nonlinear features are fused to improve predictive performance. Compared to previous studies, our model can take advantages of the combination of linear and nonlinear features between lncRNAs and diseases, and thus can effectively identify potential lncRNAdisease associations. The results show that our method achieves stateoftheart performance in the leaveoneout crossvalidation. The source codes of our method can be found at https:github.comCSUBioGroupDMFLDA2.