Rizvi, Ahsan Zaigam
- Institutionen för skoglig genetik och växtfysiologi, Sveriges lantbruksuniversitet
- Institute of Advanced Research
Sammanfattning
Long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) play critical roles in regulating biological processes and disease mechanisms through their interactions, known as lncRNA-miRNA associations (LMAs). Accurate prediction of LMAs is essential for understanding disease progression, yet existing computational methods often exhibit inadequate feature integration, limited generalizability, and insufficient modeling of complex interactions. This study introduces the Stacked Graph Attention Network with Temporal Modeling (SGAT-TM), a novel framework that integrates statistical, graph-structural, and sequence-derived features of lncRNAs and miRNAs. SGAT-TM utilizes a multilayer Graph Attention Network (GAT), enhanced by self-attention, a multilayer perceptron (MLP), and a Gated Recurrent Unit (GRU) to capture LMA patterns, preserve long-range dependencies, and produce robust representations. Evaluation on benchmark datasets demonstrates that SGAT-TM outperforms state-of-the-art methods in predictive accuracy, robustness, and generalizability. SGAT-TM enhances LMA network analysis and provides significant insights into lncRNA-miRNA associations in biological and disease contexts.
Nyckelord
Computational modeling; Diseases; Biological system modeling; Predictive models; RNA; Biological information theory; Data models; Accuracy; Regulation; Deep learning; Long non-coding RNAs; microRNAs; lncRNA-miRNA association network; graph neural networks; graph attention networks
Publicerad i
IEEE transactions on computational biology and bioinformatics
2025, volym: 22, nummer: 5, sidor: 2216-2229
Utgivare: IEEE COMPUTER SOC
SLU författare
UKÄ forskningsämne
Bioinformatik och beräkningsbiologi (Metodutveckling under 10203)
Publikationens identifierare
- DOI: https://doi.org/10.1109/TCBBIO.2025.3587877
Permanent länk till denna sida (URI)
https://res.slu.se/id/publ/144449