lncRNA-disease association prediction based on latent factor model and projection

Computer aided research of lncRNA-disease association is an important way to study the development of lncRNA-disease. The correlation analysis of existing data, the establishment of prediction model, prediction of unknown lncRNA-disease association, can make the biological experiment targeted, improve the accuracy of biological experiment. In this paper, a lncRNA-disease association prediction model based on latent factor model and projection is proposed (LFMP). This method uses lncRNA-miRNA association data and miRNA-disease association data to predict the unknown lncRNA-disease association, so this method does not need lncRNA-disease association data. The simulation results show that under the LOOCV framework, the AUC of LFMP can reach 0.8964. Better than the latest results. Through the case study of lung and colorectal tumors, LFMP can effectively infer the undetected lncRNA-disease association.

Automated summary

Computer aided research of lncRNA-disease association is an important method to study the development of lncRNA-disease. The LFMP prediction model proposed in this paper uses existing data to predict unknown lncRNA-disease associations, showing better performance than previous studies. Through case studies, LFMP effectively infers undetected lncRNA-disease associations.