Combining High Speed ELM with a CNN Feature Encoding to Predict LncRNA-Disease Associations

Abstract

Accumulated evidence indicates that lncRNAs are critical for many biological processes, especially diseases. Therefore, identifying potential lncRNA-disease associations is significant for disease prevention, diagnosis, treatment and understanding of cell life activities at the molecular level. Although novel technologies have generated considerable associations for various lncRNAs and diseases, it has inevitable drawbacks such as high cost, time consumption, and error rate. For this reason, integrating various biological databases to predict the potential association of lncRNA and disease is of great attraction. In this paper, we proposed the model called ECLDA to predict lncRNA-disease associations by combining CNN and highspeed ELM. Firstly, the feature vectors are constructed by integrating lncRNA functional similarity, disease semantic similarity and Gaussian interaction profile kernel similarity. Secondly, CNN is carried out to mine local and higher-level abstract features of the vectors. Finally, high speed ELM is used to identify the novel lncRNA disease associations. The ECLDA computational model achieved AUCs of 0.9014 in 5-fold cross validation. The results showed that ECLDA is expected to be a practical tool for biomedical research in the future.