Abstract
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.
Highlights
Computer aided research of long non-coding RNAs (lncRNAs)-disease association is an important way to study the development of lncRNA-disease
In order to solve the problem of insufficient data set of lncRNA-disease association, Zhang et al constructed a prediction model of lncRNA-disease association based on comprehensive spatial projection fraction (LDAI-ISPS)[14]
In order to evaluate the performance of latent factor model and projection is proposed (LFMP) model, we used the ROC curve and AUC value generated by Leave One Out Cross Validation (LOOCV) as the evaluation measure, and compared it with other advanced models, namely C FNBC23, NBCLDA24
Summary
Computer aided research of lncRNA-disease association is an important way to study the development of lncRNA-disease. By integrating the above information and Gaussian kernel similarity to make up for the lack of semantic similarity of disease, an accurate lncRNA-disease similarity network was reconstructed, and Laplacian regularized least squares method was used Small two multiplication is used to estimate the association between lncRNA-diseases and solve the problem of lncRNA-disease sparsity This model has some disadvantages, such as requiring a large number of combined data, and relying too much on the known lncRNA-disease association data; in view of Chen et al.’s problem, the models established by the following scholars do not need to rely on Xie et al proposed a novel prediction method of human lncRNA-disease Association (NCPHLDA)[13] based on network consistent projection. Fu et al proposed Matrix factorization-based data fusion for the prediction of lncRNA-disease associations (MFLDA)[19]. Through case studies of lung and colorectal tumors, it is proved that LFMP can effectively infer the undetected lncRNA-disease association
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