SSCMDA: spy and super cluster strategy for MiRNA-disease association prediction

Abstract

In the biological field, the identification of the associations between microRNAs (miRNAs) and diseases has been paid increasing attention as an extremely meaningful study for the clinical medicine. However, it is expensive and time-consuming to confirm miRNA-disease associations by experimental methods. Therefore, in recent years, several effective computational models for predicting the potential miRNA-disease associations have been developed. In this paper, we proposed the Spy and Super Cluster strategy for MiRNA-Disease Association prediction (SSCMDA) based on known miRNA-disease associations, integrated disease similarity and integrated miRNA similarity. For problems of mixed unknown miRNA-disease pairs containing both potential associations and real negative associations, which will lead to inaccurate prediction, spy strategy is adopted by SSCMDA to identify reliable negative samples from the unknown miRNA-disease pairs. Moreover, the super-cluster strategy could gather as many positive samples as possible to improve the accuracy of the prediction by overcoming the shortage of lacking sufficient positive training samples. As a result, the AUCs of global leave-one-out cross validation (LOOCV), local LOOCV and 5-fold cross validation were 0.9007, 0.8747 and 0.8806+/-0.0025, respectively. According to the AUC results, SSCMDA has shown a significant improvement compared with some previous models. We further carried out case studies based on various version of HMDD database to test the prediction performance robustness of SSCMDA. We also implemented case study to examine whether SSCMDA was effective for new diseases without any known associated miRNAs. As a result, a large proportion of the predicted miRNAs have been verified by experimental reports.

SMALF: miRNA-disease associations prediction based on stacked autoencoder and XGBoost

“…Li et al[ 27 ] presented MCMDA using the SVT algorithm to complete the matrix to obtain an updated miRNA-disease association matrix to predict miRNA and disease connection. Zhao et al [ 28 ] put forward the Spy and Super Cluster strategy to uncover the interaction between disease and miRNA based on the established miRNA-disease association. Furthermore, Luo et al [ 29 ] put forward KPLMS to reveal the potential connection of miRNA and disease by combining miRNA and disease through Kronecker product into the whole space and using regularized least squares to predict miRNA-disease interaction.…”

Efficient framework for predicting MiRNA-disease associations based on improved hybrid collaborative filtering

“…Plenty of evidence indicates that miRNAs play critical roles in many fundamental and important biological processes, such as immune response, transcription, proliferation and differentiation [ 4 ]. The mutation and dysregulated expression of miRNAs may be connected with the development and progression of many diseases [ 5 , 6 ]. For instance, miR-155 downregulated target gene TP53INP1 whose expression was strongly reduced in pancreatic ductal adenocarcinoma development [ 7 ].…”

MSCHLMDA: Multi-Similarity Based Combinative Hypergraph Learning for Predicting MiRNA-Disease Association

“…However, this model might cause a bias toward miRNAs with more known related diseases and might not be applicable to the diseases where associated miRNAs tend to be randomly distributed in the network. Zhao Q. et al (2018) developed a miRNAdisease association prediction method based on the Spy and super clustering strategy (SSCMDA). They used a Spy strategy to recognize trustworthy negative samples from the uncertain miRNA-disease pairs which could improve prediction accuracy.…”