Graph regularized L2,1-nonnegative matrix factorization for miRNA-disease association prediction

Abstract

Background: The aberrant expression of microRNAs is closely connected to the occurrence and development of a great deal of human diseases. To study human diseases, numerous effective computational models that are valuable and meaningful have been presented by researchers.Results: Here, we present a computational framework based on graph Laplacian regularized L 2, 1 -nonnegative matrix factorization (GRL 2, 1 -NMF) for inferring possible human disease-connected miRNAs. First, manually validated disease-connected microRNAs were integrated, and microRNA functional similarity information along with two kinds of disease semantic similarities were calculated. Next, we measured Gaussian interaction profile (GIP) kernel similarities for both diseases and microRNAs. Then, we adopted a preprocessing step, namely, weighted K nearest known neighbours (WKNKN), to decrease the sparsity of the miRNA-disease association matrix network. Finally, the GRL 2,1 -NMF framework was used to predict links between microRNAs and diseases. Conclusions:The new method (GRL 2, 1 -NMF) achieved AUC values of 0.9280 and 0.9276 in global leave-one-out cross validation (global LOOCV) and five-fold cross validation (5-CV), respectively, showing that GRL 2, 1 -NMF can powerfully discover potential disease-related miRNAs, even if there is no known associated disease.

SNFIMCMDA: Similarity Network Fusion and Inductive Matrix Completion for miRNA–Disease Association Prediction

“…For the purpose of affirming the accuracy of predicted result of SNFIMCMDA, we compared our model with three previous computational models: IMCMDA ( Chen et al, 2018b ), GRL 2,1 -NMF ( Gao et al, 2020 ), and MSCHLMDA ( Wu et al, 2020 ). Based on the verified connections of miRNA–disease that were downloaded from HMDD v2.0 database, global leave-one-out cross-validation (global LOOCV) and five-fold cross-validation (5-CV) were utilized to validate the actual performance of these computational models.…”

ILPMDA: Predicting miRNA–Disease Association Based on Improved Label Propagation

“…We first compared ILPMDA with several recent computational models [MSCHLMDA ( Wu et al, 2020 ), G R L 2,1 -NMF ( Gao et al, 2020 ), ICFMDA ( Jiang Y. et al, 2018 ), and SACMDA ( Shao et al, 2018 )] to demonstrate its superior performance via the global LOOCV and 5-CV methods. Here, multi-similarity-based combinative hypergraph learning for predicting miRNA–disease association (MSCHLMDA) applied the KNN and k-means algorithms to establish different hypergraphs, which were combined to predict potential miRNA–disease associations.…”

WVMDA: Predicting miRNA–Disease Association Based on Weighted Voting

“…Therefore, we mainly conduct comparative experiments based on five-fold cross-validation and leave-oneout cross-validation. To confirm the validity of the WVMDA prediction results, we compared our model with the previous three models: SVAEMDA (Ji et al, 2021), ICFMDA (Jiang et al, 2018), AEMDA , SACMDA (Shao et al, 2018), and GRL_2, 1-NMF (Gao et al, 2020). All models were crossvalidated to calculate TPR and FPR, draw the ROC curve, and calculate AUC (Figure 9).…”