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Review Article

机器学习方法在 Sumoylation 位点预测中的最新进展

卷 29, 期 5, 2022

发表于: 12 January, 2022

页: [894 - 907] 页: 14

弟呕挨: 10.2174/0929867328666210915112030

价格: $65

摘要

蛋白质的 Sumoylation 是蛋白质的一种重要的可逆翻译后修饰，并介导多种细胞过程。 Sumo 修饰蛋白可以改变它们的亚细胞定位、活性和稳定性。此外，它还在转录调控和信号转导等多种细胞过程中发挥重要作用。异常的 sumoylation 与许多疾病有关，包括神经退行性疾病和免疫相关疾病，以及癌症的发展。因此，SUMO 化位点（SUMO 位点）的识别对于了解其分子机制和调控作用至关重要。与劳动密集型和昂贵的实验方法相比，计算机中的 sumoylation 位点的计算预测也因其准确性、便利性和速度而备受关注。目前，许多计算预测模型已被用于识别 SUMO 站点，但其内容尚未得到全面总结和回顾。因此，本文对相关模型的研究进展进行了总结和讨论。我们主要关注基准数据集的构建、特征提取、机器学习方法、已发表的结果和在线工具，简要总结了用于 sumoylation 位点预测的生物信息学方法的发展。我们希望这篇综述能为湿实验学者提供更多帮助。

关键词: 相扑修改、特征选择、机器学习、分类、翻译后修改、顺序前向选择

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