College of Computer and Information Engineering, Inner Mongolia Agricultural University, Hohhot, China.
Inner Mongolia Autonomous Region Key Laboratory of Big Data Research and Application of Agriculture and Animal Husbandry, Hohhot, China.
J Cell Biochem. 2023 Nov;124(11):1825-1834. doi: 10.1002/jcb.30491. Epub 2023 Oct 25.
Efficient and accurate identification of antioxidant proteins is of great significance. In recent years, many models for identifying antioxidant proteins have been proposed, but the low sensitivity and high dimensionality of the models are common problems. The generalization ability of the model needs to be improved. Researchers have tried different feature extraction algorithms and feature selection algorithms to obtain the most effective feature combination and have chosen more appropriate classification algorithms and tools to improve model performance. In this article, we systematically reviewed the data set of the most frequently used antioxidant proteins and the method selection for each step of model establishment and discussed the characteristics of each method. We have conducted a detailed analysis of recent research and believe that the practical ability and efficiency of model application can be improved by reducing model dimensions. The key to improving the performance of antioxidant protein recognition models in the future may lie in feature selection, so this paper also focuses on the combination of feature extraction and selection steps in the analysis of the model building process.
高效准确地识别抗氧化蛋白具有重要意义。近年来，已经提出了许多用于识别抗氧化蛋白的模型，但模型的灵敏度低和维度高是常见问题。模型的泛化能力有待提高。研究人员尝试了不同的特征提取算法和特征选择算法，以获得最有效的特征组合，并选择了更合适的分类算法和工具来提高模型性能。在本文中，我们系统地回顾了最常用的抗氧化蛋白数据集和模型建立每个步骤的方法选择，并讨论了每种方法的特点。我们对最近的研究进行了详细分析，认为通过降低模型维度可以提高模型应用的实际能力和效率。未来提高抗氧化蛋白识别模型性能的关键可能在于特征选择，因此本文还重点分析了模型构建过程中特征提取和选择步骤的结合。
