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Academic Journal of Computing & Information Science, 2024, 7(3); doi: 10.25236/AJCIS.2024.070310.

Application of Graph Attention Networks in LncRNA Subcellular Localization Prediction


Xi Deng

Corresponding Author:
Xi Deng

Yunnan Normal University, Kunming, China


LncRNAs are crucial in gene regulation and associated with diseases and biological processes. Predicting their subcellular localization accurately remains a challenge due to sequence complexity and data imbalance. We propose a graph neural network method based on LncRNA sequence features, emphasizing enhanced prediction accuracy through optimized graph structure and attention mechanisms. Our approach addresses data imbalance by introducing a weighted graph attention mechanism and a corrective network for improved generalization with fewer samples. This study introduces a potential method for LncRNA subcellular localization prediction, highlighting GNN applicability in bioinformatics tasks. These innovations contribute to biological data analysis and understanding LncRNA function, with practical applications in experimental validation.


Long non-coding RNA (LncRNA), Graph Neural Network (GNN), Sequence feature similarity, Subcellular localization, Weighted graph attention mechanism

Cite This Paper

Xi Deng. Application of Graph Attention Networks in LncRNA Subcellular Localization Prediction. Academic Journal of Computing & Information Science (2024), Vol. 7, Issue 3: 68-72. https://doi.org/10.25236/AJCIS.2024.070310.


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