Recent Trends on the Development of Machine Learning Approaches
for the Prediction of Lysine Acetylation Sites

doi:10.2174/0929867328999210902125308
摘要

赖氨酸残基上的乙酰化被认为是最有效的蛋白质翻译后修饰之一，因为它在细胞代谢和调节过程中起着至关重要的作用。实验技术的最新进展已经解开了几种赖氨酸乙酰化底物和位点。然而，由于其成本低下，过程繁琐，耗时和劳动密集，因此已经为开发计算工具做出了一些努力。特别是，基于机器学习（ML）的方法在快速发现赖氨酸乙酰化修饰位点方面具有很大的希望，越来越多的预测工具可以见证这一点。最近，已经开发了几种ML方法用于预测赖氨酸乙酰化位点，因为它们具有时间和成本效益。在本综述中，我们提出了赖氨酸乙酰化最先进的ML预测因子的完整调查。我们将讨论开发成功预测器的各种关键方面，包括操作 ML 算法、特征选择方法、验证技术和软件实用程序。最初，我们回顾赖氨酸乙酰化位点数据库，当前的 ML 方法，工作原理，及其性能.最后，我们讨论了ML方法在预测赖氨酸乙酰化位点方面的缺点和未来方向。这篇综述可以作为实验者为他们的研究选择合适的ML工具的有用指南。此外，它可能有助于生物信息学家在蛋白质研究中开发更准确和更先进的MLbased预测因子。
关键词: 蛋白质，翻译后修饰，赖氨酸，乙酰化，机器学习，特征编码，预测模型。
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DOI https://dx.doi.org/10.2174/0929867328999210902125308	Print ISSN 0929-8673
Publisher Name Bentham Science Publisher	Online ISSN 1875-533X
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