A Novel lncRNA-Disease Association Prediction Model Using Laplacian Regularized Least Squares and Space Projection-Federated Method

Abstract

Recent studies indicated that numerous long noncoding RNAs (lncRNAs) are closely related to human diseases and can serve as potential biomarkers and drug targets for complex diseases. Therefore, identifying lncRNAs associated with diseases through computational methods is conducive to the exploration of disease pathogenesis. Most previous studies had shortcomings, such as low prediction accuracy, the need for negative samples, and weak generalization. Such studies established shallow prediction models and failed to fully capture the complex relationships among lncRNA-disease associations, lncRNA similarity, and disease similarity. LRLSSP, a new computational method based on Laplacian regularized least squares (LRLS) and space projection was used to predict candidate disease lncRNAs in this study. LRLSSP deeply integrates information on lncRNA similarity, disease similarity, and known lncRNA-disease associations. The estimated score of lncRNA-disease association was obtained through LRLS, and network projection was utilized to reliably predict disease-related lncRNAs. Leave-one-out cross validation(LOOCV) was implemented to evaluate the prediction performance of LRLSSP. Results showed that LRLSSP performed was better than other state-of-the-art methods in predicting lncRNA-disease associations. In addition, case studies conducted on melanoma,cervical cancer, ovarian cancer and breast cancer indicated that LRLSSP can discover potential and novel lncRNA-disease associations. Overall, the results demonstrated that LRLSSP may serve as a reliable and effective computational tool for disease-related lncRNAs prediction.

Keywords

• Disease similarity
• lncRNA similarity
• Laplacian regularized least squares
• space projection
• computational prediction model