In the past few years, most disease-related lncRNAs have been identified, but the experimental identification is cost-consuming and time-consuming. It is therefore very important to develop a reliable computational model to predict lncRNA-disease association. In this paper, we propose a method based on similarity, combining autoencoder and rotation forest to predict lncRNA-disease association (SARLDA). This method not only makes use of disease and lncRNA similarities, but also extracts latent low-dimension features and expand the gap between samples to make it easier to predict the association...
