标题： 机器学习技术在蛋白质磷酸化位点预测中的简要综述 显示英文标题

标题： A Brief Review of Machine Learning Techniques for Protein Phosphorylation Sites Prediction

摘要： 后翻译修饰（PTMs）在扩展蛋白质的功能多样性中起着关键作用，因此，调节原核和真核生物中的多种细胞过程。磷酸化修饰是一种重要的PTM，发生在大多数蛋白质中，并在许多生物过程中发挥重要作用。磷酸化过程的紊乱会导致包括神经障碍和癌症在内的多种疾病。首先，本研究全面回顾了所有与磷酸化位点（p-位点）相关的数据库。其次，我们介绍了p-位点预测中的数据集创建、数据预处理和方法评估的所有步骤。接下来，我们研究了属于两种计算和机器学习（ML）组的p-位点预测方法。此外，显示通过ML进行p-位点预测基本上有两种主要方法：传统方法和端到端学习，对这两种方法都进行了概述。此外，本研究介绍了在ML方法中大部分使用的最重要的特征提取技术。最后，我们根据dbPTM数据库2022版新发布的蛋白质，基于一般物种和人类物种创建了三个测试集。在测试集上评估可用的在线工具后，结果表明在线工具在新报告的磷酸化蛋白质的p-位点预测方面的性能相当薄弱。 显示英文摘要

摘要： Post-translational modifications (PTMs) have vital roles in extending the functional diversity of proteins and as a result, regulating diverse cellular processes in prokaryotic and eukaryotic organisms. Phosphorylation modification is a vital PTM that occurs in most proteins and plays significant roles in many biological processes. Disorders in the phosphorylation process lead to multiple diseases including neurological disorders and cancers. At first, this study comprehensively reviewed all databases related to phosphorylation sites (p-sites). Secondly, we introduced all steps regarding dataset creation, data preprocessing and method evaluation in p-sites prediction. Next, we investigated p-sites prediction methods which fall into two computational and Machine Learning (ML) groups. Additionally, it was shown that there are basically two main approaches for p-sites prediction by ML: conventional and End-to-End learning, which were given an overview for both of them. Moreover, this study introduced the most important feature extraction techniques which have mostly been used in ML approaches. Finally, we created three test sets from new proteins related to the 2022th released version of the dbPTM database based on general and human species. After evaluating available online tools on the test sets, results showed that the performance of online tools for p-sites prediction are quite weak on new reported phospho-proteins.