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.

Predicting LncRNAdisease Association by Autoencoder and Rotation Forest In the past few years, most diseaserelated lncRNAs have been identified, but the experimental identification is costconsuming and timeconsuming. It is therefore very important to develop a reliable computational model to predict lncRNAdisease association. In this paper, we propose a method based on similarity, combining autoencoder and rotation forest to predict lncRNAdisease association (SARLDA). This method not only makes use of disease and lncRNA similarities, but also extracts latent lowdimension features and expand the gap between samples to make it easier to predict the associations. To evaluate our method, we conducted several experiments. Sufficient validations show that this method has significantly improved the prediction performance.