LDA-SCGB: inferring lncRNA-disease associations based on condensed gradient boosting.

Background: Long non-coding RNAs (lncRNAs) play essential roles in various physiological and pathological processes. Inferring new lncRNA-disease associations (LDAs) not only promotes us to better understand these complex biological processes, but also provides new options for the diagnosis and prevention of diseases.

Results: A novel computational model, LDA-SCGB, is proposed to predict new LDAs. LDA-SCGB first extracts features of each lncRNA-disease pair with singular value decomposition. Next, it classifies unknown lncRNA-disease pairs through the condensed gradient boosting model. The results demonstrated that LDA-SCGB greatly outperformed the other four representative LDA inference methods (SDLDA, LDNFSGB, LDAenDL and LDASR) under 5-fold cross validations on lncRNAs, diseases, and lncRNA-disease pairs on three LDA datasets, which were from lncRNADisease v2.0, MNDR, and lncRNADisease v3.0, respectively. LDA-SCGB was further used to find potential lncRNAs for colorectal cancer, heart failure, and lung adenocarcinoma. The results demonstrated that CCDC26, MIAT, and CCDC26 had higher association probability with colorectal cancer, heart failure, and lung adenocarcinoma, respectively.

Conclusions: We foresee that LDA-SCGB was capable of predicting potential lncRNAs for complex diseases and further assisting in cancer diagnosis and therapy.