Integrating multiple microRNA functional similarity networks for improved disease-microRNA association prediction

Abstract

MicroRNAs (miRNAs) play a critical role in disease mechanisms, making the identification of disease-associated miRNAs essential for precision medicine. We propose a novel computational method, multiplex-heterogeneous network for MiRNA-disease associations (MHMDA), which integrates multiple miRNA functional similarity networks and a disease similarity network into a multiplex-heterogeneous network. This approach employs a tailored random walk with restart algorithm to predict disease-miRNA associations, leveraging the complementary information from experimentally validated and predicted miRNA-target interactions, as well as disease phenotypic similarities. Evaluated on the human microRNA disease database and miR2Disease datasets using leave-one-out cross-validation and 5-fold cross-validation, MHMDA demonstrates superior performance, achieving area under the receiver operating characteristic curve values of 0.938 and 0.913 on human microRNA disease database and miR2Disease, respectively, and outperforming existing methods. The integration of multiplex networks enhances prediction accuracy by capturing diverse miRNA functional relationships, which directly contributes to the high area under the receiver operating characteristic curve and area under the precision-recall curve values observed. Additionally, MHMDA's stability across parameter variations and disease contexts underscores its robustness and potential for real-world applications in identifying novel disease-miRNA associations.

Keywords: MHMDA method; miRNA functional similarity; miRNA-disease association; multiplex-heterogeneous networks; random walk with restart algorithm.

Figure 1

Construction of disease and miRNA Networks for MHMDA. This figure illustrates the construction of various networks used in MHMDA, with nodes representing miRNAs or diseases and edges representing interactions or associations. (a) miRNA monoplex network (MonoNet_miRWalk) constructed from the experimentally validated miRNA-target database miRWalk. (b) miRNA monoplex network (MonoNet_TargetScan) constructed from the predicted miRNA-target database TargetScan. (c) Integrated miRNA monoplex network (MonoNet_Integrated) constructed by combining MonoNet_miRWalk and MonoNet_TargetScan. (d) Heterogeneous network (DiSimNet-MonoNet_miRWalk or DiSimNet-MonoNet_TargetScan) formed by connecting a miRNA monoplex network, the disease similarity network, and known disease-miRNA associations. (e) miRNA multiplex network (MultiNet_miRNA) composed of the two miRNA monoplex networks (MonoNet_miRWalk and MonoNet_TargetScan). (f) Multiplex-heterogeneous network (DiSimNet-MultiNet_miRNA) formed by connecting the disease similarity network and the multiplex network (MultiNet_miRNA) using known disease-miRNA associations

Figure 2

Prediction performance of MHMDA-M and MHMDA-MH across parameter settings. This figure shows the prediction performance (AUROC) of MHMDA-M (miRNA multiplex network) and MHMDA-MH (multiplex-heterogeneous network) as a function of parameter changes, with line plots where the x-axis represents the parameter value and the y-axis represents AUROC. (a) Performance on the HMDD dataset with varying restart probability (γ). (b) Performance on the HMDD dataset with varying jumping probability (δ) between miRNA networks. (c) Performance on the miR2Disease dataset with varying restart probability (γ). (d) Performance on the miR2Disease dataset with varying jumping probability (δ) between miRNA networks. (e) Performance on the HMDD dataset with varying jumping probability between disease and miRNA networks (λ). (f) Performance on the HMDD dataset with varying importance weight of disease vs. miRNA seeds ($\eta$). (g) Performance on the miR2Disease dataset with varying jumping probability between disease and miRNA networks (λ). (h) Performance on the miR2Disease dataset with varying importance weight of disease vs. miRNA seeds ($\eta$)

Figure 2

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Figure 3

Performance comparison of MHMDA-M and RWRMDA on miRNA multiplex and monoplex networks. This figure compares the performance of MHMDA-M (MultiNet_miRNA) and RWRMDA (MonoNet_miRWalk, MonoNet_TargetScan, MonoNet_Integrated) using AUROC and AUPRC, with ROC curves (panels a and b) and precision-recall curves (panels c and d). (a) ROC curves for the HMDD dataset, with the x-axis as FPR and the y-axis as TPR. (b) ROC curves for the miR2Disease dataset. (c) Precision-recall curves for the HMDD dataset, with the x-axis as recall TPR and the y-axis as precision. (d) Precision-recall curves for the miR2Disease dataset. Color scheme: MHMDA-M in red, RWRMDA (MonoNet_miRWalk) in blue, RWRMDA (MonoNet_TargetScan) in purple, and RWRMDA (MonoNet_Integrated) in green

Figure 4

Performance comparison of MHMDA-MH and RWRHMDA on multiplex-heterogeneous and heterogeneous networks. This figure compares the performance of MHMDA-MH (DiSimNet-MultiNet_miRNA) and RWRHMDA (DiSimNet-MonoNet_miRWalk, DiSimNet-MonoNet_TargetScan) using AUROC and AUPRC, with ROC curves (panels a and b) and precision-recall curves (panels c and d). (a) ROC curves for the HMDD dataset, with the x-axis as FPR and the y-axis as TPR. (b) ROC curves for the miR2Disease dataset. (c) Precision-recall curves for the HMDD dataset, with the x-axis as recall TPR and the y-axis as precision. (d) Precision-recall curves for the miR2Disease dataset. Color scheme: MHMDA-MH in red, RWRHMDA (DiSimNet-MonoNet_miRWalk) in green, and RWRHMDA (DiSimNet-MonoNet_TargetScan) in blue

Figure 5

Evaluation of prediction performance across diverse disease characteristics. (a) Correlation between AUROC and the number of known disease-associated miRNAs, showing a negligible correlation. (b) Average AUROC across disease categories (e.g. cancer, cardiovascular, immunological, metabolic). (c) Average AUROC across tissue types (e.g. blood, bone, brain, heart)