Fig 1.
The workflow of our proposed HGCLAMIR model for predicting potential miRNA-disease associations.
Fig 2.
The detailed description of different modules in HGCLAMIR.
(A) Illustration of hypergraph contrastive learning. (B) Introduction of integrated representation learning.
Fig 3.
The influence of different hyperparameters on HGCLAMIR based on the MDAv2.0 dataset under 5-fold cross-validation.
(A) The impact of hyperparameter k on HGCLAMIR. (B) The impact of hyperparameter c on HGCLAMIR.
Table 1.
The prediction performance of all models evaluated by 5-fold cross-validation five times.
Fig 4.
ROC curves and PR curves performed by HGCLAMIR based on the MDAv2.0 dataset under 5-fold cross-validation.
Fig 5.
ROC curves and PR curves performed by HGCLAMIR and ten baseline models based on the MDAv2.0 dataset under 5-fold cross-validation.
Table 2.
The prediction performance of all models based on an independent dataset.
Table 3.
The prediction performance of ablation experiment evaluated by 5-fold cross-validation five times.
Table 4.
Top 50 breast neoplasms-related miRNAs predicted by HGCLAMIR based on the MDAv2.0 dataset.
Note that the number in evidence means PubMed Unique Identifier (PMID).
Fig 6.
The biological analysis of hsa-mir-371a associated with breast neoplasms.
(A) The enrichment analysis of target gene sets related to hsa-mir-371a. (B) The survival analysis based on hsa-mir-371a expression levels.