Jul 2026· Journal of Mechanics in Medicine and Biology· 0 citations
TL;DR
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.
Abstract
Predicting potential associations between microRNAs (miRNAs) and diseases is essential for deciphering complex pathogenic mechanisms and advancing personalized medicine. However, many existing computational methods fail to adequately capture high-order topological structures within heterogeneous biological networks, limiting their predictive performance. In this study, we propose GCN-XGB, a novel hybrid computational framework that integrates a two-layer Graph Convolutional Network (GCN) with Extreme Gradient Boosting (XGBoost) to improve the accuracy of miRNA-disease association prediction. Specifically, we first input k-mer derived initial features of miRNAs, BioBERT-derived initial features of diseases, and the computed miRNA-miRNA similarity, disease-disease similarity, and miRNA-disease association information into the two-layer GCN. Through deep feature propagation and aggregation of second-order neighbor information, the GCN integrates these inputs to generate enhanced discriminative high-dimensional embeddings, providing more informative and discriminative features for the downstream XGBoost classifier. Experimental results under rigorous 10-fold cross-validation demonstrate that GCN-XGB consistently outperforms several state-of-the-art baseline models when all models are trained on the same GCN-enhanced features, achieving a superior AUC of 0.9601 and AUPR of 0.9587. Furthermore, case studies on prevalent neoplasms (e.g., Breast, Colon, and Lung Cancer) confirm the framework's efficacy in discovering novel associations even in the absence of prior clinical data. Our findings suggest that GCN-XGB is a powerful and reliable tool for identifying potential disease-related miRNAs and prioritizing candidates for experimental validation.
A novel GNN framework, APKAGN, designed for predicting miRNA-disease associations significantly enhances the accuracy of MDAs prediction, offering a powerful tool for investigating disease mechanisms and identifying biomarkers.
Ru Nie, Yingkai Li, Zhengwei Li et al.· IEEE transactions on computa...· 0 citations
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.
Y. Sun, Wenjing Su, Si-Qi Zhu et al.· Bioinform.· 0 citations
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.
Peng Qin, Jiyong An· International Journal of Mol...· 0 citations
RGCNMDA is a leakage-controlled multi-view framework that integrates global latent structure, local profiles and similarities, and pathway context that supports the robustness of leakage-controlled multi-view learning across standard and cold-start evaluation settings.
Chao Hou, Mohamed Kone, Yang Xiang et al.· Bioinformatics· 0 citations
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.
The human microbiota is essential for maintaining physiological homeostasis, and microbial dysbiosis is increasingly implicated in the pathogenesis of complex diseases. Identifying potential microbe-disease associations (MDAs) can therefore facilitate mechanistic investigation, biomarker discovery, and therapeutic development. However, wet-laboratory validation is costly and time-consuming, while existing computational methods often struggle with sparse association networks and complex nonlinear interactions. We propose a novel deep learning approach named the Dual Dynamic Hypergraph Convolution Framework for Human Microbe-Disease Association Prediction (DDHMDA). Specifically, DDHMDA first utilizes graph convolutional networks to encode local topological features. Subsequently, it dynamically constructs a dual hypergraph architecture: a differentiable K-means similarity hypergraph to capture intra-modal global clustering patterns, and an attention-based cross-modal interaction hypergraph to model inter-modal interactions synergistically. Under leakage-free pair-level five-fold cross-validation (denoted as CV3), DDHMDA achieved AUC/AUPR values of 0.9789 ± 0.0177/0.9843 ± 0.0129 on HMDAD and 0.9651 ± 0.0042/0.9740 ± 0.0031 on Disbiome. DDHMDA also obtained the best overall CV3 performance among the eight evaluated methods. Furthermore, ablation experiments and case studies validate the practical effectiveness of individual modules and the biological interpretability in discovering novel MDAs. Therefore, DDHMDA would be a reliable tool for identifying potential MDAs.
Zhi Wu, Zhaohui Liao· Mathematics· 0 citations
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