Long non-coding RNAs (lncRNAs) play a broad spectrum of distinctive regulatory roles through interactions with proteins. However, only a few plant lncRNAs have been experimentally characterized. We propose GPLPI, a graph representation learning method, to predict plant lncRNA-protein interaction (LPI) from sequence and structural information. GPLPI employs a generative model using long short-term memory (LSTM) with graph attention. Evolutionary features are extracted using frequency chaos game representation (FCGR). Manifold regularization and l2-norm are adopted to obtain discriminant feature representations and mitigate overfitting. The model captures locality preserving and reconstruction constraints that lead to better generalization ability. Finally, potential interactions between lncRNAs and proteins are predicted by integrating catboost and regularized Logistic regression based on L-BFGS optimization algorithm. The method is trained and tested on Arabidopsis thaliana and Zea mays datasets. GPLPI achieves accuracies of 85.76% and 91.97% respectively. The results show that our method consistently outperforms other state-of-the-art methods.

中文翻译：

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带有图注意力的植物lncRNA-蛋白质相互作用预测的深度学习模型。

长的非编码RNA（lncRNA）通过与蛋白质的相互作用发挥广泛的独特调节作用。然而，仅少数植物lncRNA已通过实验表征。我们提出GPLPI，一种图形表示学习方法，以从序列和结构信息预测植物lncRNA-蛋白质相互作用（LPI）。GPLPI采用生成模型，使用长短期记忆（LSTM）并注意图形。使用频率混沌游戏表示（FCGR）提取进化特征。采用流形正则化和l2-范数来获得判别式特征表示并减轻过度拟合。该模型捕获了局部保存和重建约束，这些约束导致更好的泛化能力。最后，通过整合catboost和基于L-BFGS优化算法的正则Logistic回归，可以预测lncRNA与蛋白质之间的潜在相互作用。该方法在拟南芥和玉米数据集上经过培训和测试。GPLPI的准确度分别达到85.76％和91.97％。结果表明，我们的方法始终优于其他最新方法。