Phosphorylation is the most important and studied post-translational modification (PTM), which plays a crucial role in protein function studies and experimental design. Many significant studies have been performed to predict phosphorylation sites using various machine-learning methods. Recently, several studies have claimed that deep learning-based methods are the best way to predict the phosphorylation sites because deep learning as an advanced machine learning method can automatically detect complex representations of phosphorylation patterns from raw sequences and thus offers a powerful tool to improve phosphorylation site prediction. In this study, we report DF-Phos, a new phosphosite predictor based on the Deep Forest to predict phosphorylation sites. In DF-Phos, the feature vector taken from the CkSAApair method is as input for a Deep Forest framework for predicting phosphorylation sites. The results of 10-fold cross-validation show that the Deep Forest method has the highest performance among other available methods. We implemented a Python program of DF-Phos, which is freely available for non-commercial use at https://github.com/zahiriz/DF-Phos Moreover, users can use it for various PTM predictions.

中文翻译：

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DF-Phos：通过深层森林预测蛋白质磷酸化位点

磷酸化是最重要和研究最多的翻译后修饰（PTM），它在蛋白质功能研究和实验设计中起着至关重要的作用。已经进行了许多重要的研究来使用各种机器学习方法来预测磷酸化位点。最近，一些研究声称基于深度学习的方法是预测磷酸化位点的最佳方法，因为深度学习作为一种先进的机器学习方法可以自动检测原始序列中磷酸化模式的复杂表示，从而为改善磷酸化提供了强大的工具站点预测。在这项研究中，我们报告了 DF-Phos，一种基于 Deep Forest 的新型磷酸位点预测器，用于预测磷酸化位点。在 DF-Phos 中，从 CkSAApair 方法获取的特征向量作为用于预测磷酸化位点的 Deep Forest 框架的输入。10倍交叉验证的结果表明，深度森林方法在其他可用方法中具有最高的性能。我们实现了 DF-Phos 的 Python 程序，该程序可免费用于非商业用途，网址为 https://github.com/zahiriz/DF-Phos 此外，用户可以将其用于各种 PTM 预测。