Ning Qiao, Qi Zedong
Information Science and Technology, Dalian Maritime University, Dalian, Liaoning, China; The School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, China; Neusoft Education Technology Group, Dalian, China; Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, China.
Information Science and Technology, Dalian Maritime University, Dalian, Liaoning, China.
Comput Biol Med. 2025 Jan;184:109328. doi: 10.1016/j.compbiomed.2024.109328. Epub 2024 Nov 14.
As an important post-translational modification, glutarylation plays a crucial role in a variety of cellular functions. Recently, diverse computational methods for glutarylation site identification have been proposed. However, the class imbalance problem due to data noise and uncertainty of non-glutarylation sites remains a great challenge. In this article, we propose a novel semi-supervised learning algorithm, called WGAN-GP_Glu, for identifying reliable non-glutarylation lysine sites from those without glutarylation annotation. WGAN-GP_Glu method is a multi-module framework algorithm, which mainly includes a reliable negative sample selection module, a deep feature extraction module, and a glutarylation site prediction module. In reliable negative sample selection module, we design an improved method of Wasserstein GAN with Gradient Penalty (WGAN-GP), named ReliableWGAN-GP, including three parts, two generators G1, G2 and a discriminator D, which can select reliable non-glutarylation samples from a great number of unlabeled samples. Generator G1 is utilized to generate noise data from unlabeled samples. For generator G2, both the positive sample and the noise data are used as inputs to improve the discriminant capability of discriminator D. Then, convolutional neural network and bidirectional long short-term memory network combined with attention mechanism are utilized to extract deep features for glutarylation samples and reliable non-glutarylation samples. Finally, a glutarylation site prediction module based on the three-layer fully connected layer is designed to make class predictions for samples. The sensitivity, specificity, accuracy and Matthew correlation coefficient of WGAN-GP_Glu on the independent test data set reach 90.58 %, 95.82 %, 94.44 % and 0.8645, respectively, which surpassed the existing methods for glutarylation sites prediction. Therefore, WGAN-GP_Glu can serve as a powerful tool in identifying glutarylation sites and the ReliableWGAN-GP algorithm is effective in selecting reliable negative samples. The data and code are available at https://github.com/xbbxhbc/WGAN-GP_Glu.git.
作为一种重要的翻译后修饰，戊二酰化在多种细胞功能中发挥着关键作用。近年来，已提出了多种用于戊二酰化位点识别的计算方法。然而，由于数据噪声和非戊二酰化位点的不确定性导致的类不平衡问题仍然是一个巨大的挑战。在本文中，我们提出了一种新颖的半监督学习算法，称为WGAN-GP_Glu，用于从没有戊二酰化注释的位点中识别可靠的非戊二酰化赖氨酸位点。WGAN-GP_Glu方法是一种多模块框架算法，主要包括可靠负样本选择模块、深度特征提取模块和戊二酰化位点预测模块。在可靠负样本选择模块中，我们设计了一种改进的带梯度惩罚的Wasserstein生成对抗网络（WGAN-GP）方法，称为可靠WGAN-GP，包括三个部分，两个生成器G1、G2和一个判别器D，它可以从大量未标记样本中选择可靠的非戊二酰化样本。生成器G1用于从未标记样本中生成噪声数据。对于生成器G2，正样本和噪声数据都用作输入，以提高判别器D的判别能力。然后，利用卷积神经网络和结合注意力机制的双向长短期记忆网络为戊二酰化样本和可靠的非戊二酰化样本提取深度特征。最后，设计了一个基于三层全连接层的戊二酰化位点预测模块对样本进行类别预测。WGAN-GP_Glu在独立测试数据集上的灵敏度、特异性、准确率和马修相关系数分别达到90.58%、95.82%、94.44%和0.8645，超过了现有的戊二酰化位点预测方法。因此，WGAN-GP_Glu可以作为识别戊二酰化位点的有力工具，并且可靠WGAN-GP算法在选择可靠负样本方面是有效的。数据和代码可在https://github.com/xbbxhbc/WGAN-GP_Glu.git获取。
