SKF-LDA: Similarity Kernel Fusion for Predicting lncRNA-Disease Association.

ABSTRACT: Recently, prediction of lncRNA-disease associations has attracted more and more attentions. Various computational models have been proposed; however, there is still room to improve the prediction accuracy. In this paper, we propose a kernel fusion method with different types of similarities for the lncRNAs and diseases. The expression similarity and cosine similarity are used for lncRNAs, and the semantic similarity and cosine similarity are used for the diseases. To eliminate the noise effect, a neighbor constraint is enforced to refine all the similarity matrices before fusion. Experimental results show that the proposed similarity kernel fusion (SKF)-LDA method has the superiority performance in terms of AUC values and other measurements. In the schemes of LOOCV and 5-fold CV, AUC value

SUBMITTER: Xie G 

PROVIDER: S-EPMC6742806 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature