Predicting miRNA-disease associations based on adaptive neighborhood propagation and feature spatial recombination.

A Hybrid Graph Convolutional Network and XGBoost Framework for miRNA–Disease Association Prediction

GCN-XGB is a novel hybrid computational framework that integrates a two-layer Graph Convolutional Network with Extreme Gradient Boosting to improve the accuracy of miRNA-disease association prediction and is suggested to be a powerful and reliable tool for identifying potential disease-related miRNAs and prioritizing candidates for experimental validation.

MRGBMDAT: a multi-relational graph encoder network with bilinear fusion for miRNA-disease association type prediction

MRGBMDAT, a multi-relational graph encoder network with bilinear fusion for miRNA-disease association type prediction significantly outperforms five state-of-the-art methods across multiple evaluation metrics, exhibiting strong discriminative power and generalization capability.

Computational Method Using Attribute-Aware Message Passing and Graph Convolutional Network for Potential miRNA–Disease Association Prediction

MicroRNA (miRNA) dysregulation is a crucial pathogenic factor that extensively participates in the occurrence and progression of various human diseases, especially cancers. Identifying unknown miRNA–disease connections is essential for understanding disease pathogenesis and improving clinical treatment strategies. Traditional biological experiments are often expensive and technically restricted, so computational prediction has become a widely used auxiliary research tool. In this study, we develop a novel predictive model called Attribute-Aware Message Passing Graph Convolutional Network (AAMPGCN) to identify potential miRNA–disease associations. The advantage of AAMPGCN lies in integrating miRNA and disease attribute information into the message-passing process: it partitions the miRNA–disease heterogeneous graph that incorporates miRNA functional similarity, disease semantic similarity, and Gaussian interaction kernel similarity into attribute-homogeneous subgraphs, while restricting high-order message propagation within each subgraph. This mechanism effectively filters cross-attribute noise, preserves the discriminability of miRNA and disease embeddings during deep convolution, and is thus well-adapted to miRNA–disease heterogeneous networks. The AAMPGCN prioritizes miRNA and disease attributes, aggregating messages specifically among nodes with similar attribute characteristics that are relevant to miRNA–disease interactions. Experimental results show that the AAMPGCN model achieves AUC and AUPR values of 94.06 and 93.52 on the HMDD2.0 dataset, which outperforms existing methods. The proposed AAMPGCN provides a new and effective method for miRNA–disease association prediction, and also offers theoretical support for the research on disease molecular mechanisms and the screening of clinical therapeutic targets.

Integrating lncRNA data for prediction of miRNA-disease association using network fusion and matrix completion

A novel computational framework based on network fusion and matrix completion for miRNA-disease association prediction, which integrates heterogeneous biological information, including miRNA-disease, lncRNA-disease, and miRNA-lncRNA associations, together with disease semantic similarity and miRNA/lncRNA functional similarity is proposed.

MFCAMNet: predicting miRNA-disease associations by multi-feature cascade attention mechanism network

This work proposes MFCAMNet, a Multi-Feature fusion and Cross-Self-Attention model for MiRNA–Disease association prediction, which employs element-level addition, element-level multiplication, and concatenation operations to generate miRNA-disease pair features with rich information.

Heterogeneous dual-channel and interpretable graph representation learning with global virtual nodes for microRNA-mediated drug sensitivity prediction.