Lysine glycation is one of the most significant protein post-translational modifications, which changes the properties of the proteins and causes them to be dysfunctional. Accurately identifying glycation sites helps to understand the biological function and potential mechanism of glycation in disease treatments. Nonetheless, the experimental methods are ordinarily inefficient and costly, so effective computational methods need to be developed. In this study, we proposed the new model called iGly-IDN based on the improved densely connected convolutional networks (DenseNet). First, one hot encoding was adopted to obtain the original feature maps. Afterward, the improved DenseNet was adopted to capture feature information with the importance degrees during the feature learning. According to the experimental results, Acc reaches 66%, and Mathews correlation coefficient reaches 0.33 on the independent testing data set, which indicates that the iGly-IDN can provide more effective glycation site identification than the current predictors.

中文翻译：

---

iGly-IDN：基于改进的 DenseNet 识别蛋白质中的赖氨酸糖化位点。

赖氨酸糖化是最重要的蛋白质翻译后修饰之一，它改变蛋白质的特性并导致它们功能失调。准确识别糖化位点有助于了解糖化在疾病治疗中的生物学功能和潜在机制。尽管如此，实验方法通常效率低下且成本高昂，因此需要开发有效的计算方法。在本研究中，我们基于改进的密集连接卷积网络（DenseNet）提出了名为 iGly-IDN 的新模型。首先，采用一种热编码来获得原始特征图。随后，在特征学习过程中，采用改进的DenseNet来捕获具有重要程度的特征信息。根据实验结果，在独立测试数据集上，Acc 达到 66%，Mathews 相关系数达到 0.33，这表明 iGly-IDN 可以比当前的预测器提供更有效的糖化位点识别。