DNI-MDCAP: improvement of causal MiRNA-disease association prediction based on deep network imputation

Abstract

Background: MiRNAs are involved in the occurrence and development of many diseases. Extensive literature studies have demonstrated that miRNA-disease associations are stratified and encompass ~ 20% causal associations. Computational models that predict causal miRNA-disease associations provide effective guidance in identifying novel interpretations of disease mechanisms and potential therapeutic targets. Although several predictive models for miRNA-disease associations exist, it is still challenging to discriminate causal miRNA-disease associations from non-causal ones. Hence, there is a pressing need to develop an efficient prediction model for causal miRNA-disease association prediction.

Results: We developed DNI-MDCAP, an improved computational model that incorporated additional miRNA similarity metrics, deep graph embedding learning-based network imputation and semi-supervised learning framework. Through extensive predictive performance evaluation, including tenfold cross-validation and independent test, DNI-MDCAP showed excellent performance in identifying causal miRNA-disease associations, achieving an area under the receiver operating characteristic curve (AUROC) of 0.896 and 0.889, respectively. Regarding the challenge of discriminating causal miRNA-disease associations from non-causal ones, DNI-MDCAP exhibited superior predictive performance compared to existing models MDCAP and LE-MDCAP, reaching an AUROC of 0.870. Wilcoxon test also indicated significantly higher prediction scores for causal associations than for non-causal ones. Finally, the potential causal miRNA-disease associations predicted by DNI-MDCAP, exemplified by diabetic nephropathies and hsa-miR-193a, have been validated by recently published literature, further supporting the reliability of the prediction model.

Conclusions: DNI-MDCAP is a dedicated tool to specifically distinguish causal miRNA-disease associations with substantially improved accuracy. DNI-MDCAP is freely accessible at http://www.rnanut.net/DNIMDCAP/ .

Keywords: Causal miRNA-disease association prediction; Deep graph embedding; Network imputation; miRNA; miRNA-disease association.

Fig. 1

Workflow of DNI-MDCAP

Fig. 2

ROC curves performed by DNI-MDCAP: a ROC curve of tenfold cross-validation. b ROC curve of independent test

Fig. 3

Improved predictive performance of DNI-MDCAP: a ROC curves of DNI-MDCAP (with or without imputation) and previous models in discriminating causal miRNA-disease associations from the non-causal associations. b Violin plots of DNI-MDCAP showing the distribution of model prediction scores in the causal, non-causal and non-disease groups

Fig. 4

The query interface and sample result of DNI-MDCAP server