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, Wen-Jing Su, Si-Qi Zhu et al.· Bioinform.· 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 c...
Jie Zhou, Pei-Shen Yan, Jia Qu et al.· Journal of Mechanics in Medi...· 0 citations
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 s...
Habib MotieGhader, Seyed Mehdi Jazayeri, Reyhaneh Sadat Jazayeri· Computational and Systems On...· 0 citations
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, Ji-Yong An· International Journal of Mol...· 0 citations
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. B. Navya, Geethu Muraleedharan, P. K. Fasna et al.· Biochemical and Biophysical...· 0 citations
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