HSAELDA: Predicting lncRNA-disease associations based on heterogeneous networks and Stacked Autoencoder

It is well known that the study of the lncRNA-disease associations (LDAs) is of great value for the diagnosis and cure of many complex diseases. However, exploring unknown LDAs is extremely difficult and expensive. Therefore, it is essential to find a more accurate and effective calculation method to predict the potential LDAs. However, most previous studies focused on designing complex similarity-based methods to predict the potential interaction between lncRNAs and diseases. In this research, combining the three biological networks of lncRNA-disease, miRNA-lncRNA and miRNA-disease, a new computing model based on heterogeneous networks and stacked autoencoder (SAE) is proposed, called HSAELDA. Then, the SAE is used to extract the comprehensive features of the lncRNA-disease pairs, the LightGBM classifier is used for training. At the same time, five-fold cross-validation (CV) is used to compare our model with some existing prediction methods. The final comparison results showed HSAELDA obtained the highest AUC value of 0.978. In conclusion, the overall prediction performance of HSAELDA has been greatly improved compared to the state-of-art models. Experimental results and case study results show that HSAELDA is an effective method for predicting potential LDAs.