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.
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, Siqi Zhu et al.· Bioinformatics· 0 citations
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.
Jie Zhou, Peishen Yan, Jia Qu et al.· Journal of Mechanics in Medi...· 0 citations
Stomach cancer (SC) or gastric cancer (GC) is one of the most common gastrointestinal malignancies. Currently, some studies based on competing endogenous RNAs (ceRNA) network analysis have been assessed for this cancer. The construction and analysis of ceRNA network is a novel approach for identification of RNA‐based biomarkers in cancer studies. In the current study, our aim is to discover novel potential biomarker based on lncRNA, miRNA, and mRNA ceRNA network analysis. To do this, differentially expressed miRNAs (DEmiRNAs) between SC and normal samples have been collected from the DBDEMC website. Then, a list of lncRNAs regulating DEmiRNA is collected from the LncACTdb 3.0 database and the lncRNA–miRNA bipartite network has been constructed. Subsequently, target mRNAs of the DEmiRNAs have been collected from the miRTarBase website and a protein–protein interaction (PPI) network was constructed thanks to the STRING website. Afterwards, five gene modules were discovered from the PPI network. Thus, miRNA–mRNA bipartite network has been constructed for DEmiRNAs and modules' genes. Finally, lncRNA–miRNA–mRNA ceRNA network is constructed and analyzed. After analyzing the ceRNA network, it is observed that
DANCR
and
MEG3
with degree three and
XIST
,
MALAT1
,
TTN‐AS1
,
HOTAIR
,
HCAL
,
GAS5
with degree two, have the important role in the ceRNA network regulation. As well as,
miR‐145‐5p, miR‐145‐3p, miR‐183‐5p, miR‐21‐5p
are hub miRNAs in the ceRNA network and they are targeting more genes of the PPI modules' genes. Among the genes evaluated, miR‐96‐5p and miR‐183‐5p were the only candidates that exhibited significant differential expression between the normal and SC groups and showed a significant association with overall survival (OS) in patients with stomach cancer (log‐rank
p
< 0.05), underscoring their potential prognostic significance.
Habib Motieghader, Seyed Mehdi Jazayeri, Reyhaneh Sadat Jazayeri· Computational and Systems On...· 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
Cervical cancer, the third most common cancer worldwide with 50% increase in mortality, is a growing public health concern. Recent research on non-coding RNAs including long coding RNAs (lncRNAs) and circular RNAs (circRNAs) highlights their key roles in carcinogenesis across different cancers; but the studies on lncRNA-circRNA-mRNA regulatory networks (tripartite network) are still lacking in cervical cancer. This study integrates transcriptomic data to analyse circRNA-lncRNA-mRNA interactions, focusing on immune signaling pathways. To further understand the biological relevance of these interactions, differentially expressed genes were identified using DESeq2/limma, followed by functional enrichment. A competing endogenous RNA (ceRNA) network was constructed in Cytoscape, with miRNA binding serving as the central connecting factor. The immune-focused subnetwork comprised 7 mRNAs, 14 circRNAs, and 80 lncRNAs. Different machine learning prognostic models based on the ncRNA network and individual RNAs including differentially expressed coding and non-coding RNAs were developed to assess prognostic performance. Among these, the LASSO model based on the circRNA-lncRNA-mRNA network showed the best overall performance, with AUC values of 0.77 in the training set and 0.89 in the test set. Model performance was also assessed using bootstrap resampling for internal validation to ensure robustness. DLEU1, ITPR1, hsa_circRNA_101206, and miR-210 were identified as key prognostic biomarkers. Docking, HPA validation, and immune infiltration analyses confirmed the miR-210:DLEU1:ITPR1 axis as a key immune regulator in cervical cancer. This network modelling approach establishes that combinatorial modelling of coding and non-coding elements outperforms individual approaches, providing a robust framework for prognostic stratification and therapeutic targeting in cervical cancer.
V. Navya, Geethu Muraleedharan, P. Fasna et al.· Biochemical and Biophysical...· 0 citations
