RF-Phos: A Novel General Phosphorylation Site Prediction Tool Based on Random Forest.
RF-Phos: A Novel General Phosphorylation Site Prediction Tool Based on Random Forest.
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DOI：
10.1155/2016/3281590
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发表时间：
2016
影响因子：
--
通讯作者：
Kc DB
中科院分区：
文献类型：
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作者：
Ismail HD;Jones A;Kim JH;Newman RH;Kc DB
蛋白质磷酸化是真核生物中最广泛的调节机制之一。在过去的十年中，磷酸化位点预测已成为生物信息学领域的一个重要问题。在这里，我们报告了一种新方法，称为基于随机森林的磷酸盐预测器 2.0 (RF-Phos 2.0)，仅以蛋白质的一级氨基酸序列作为输入来预测磷酸化位点。 RF-Phos 2.0 使用具有序列和结构特征的随机森林，能够识别许多蛋白质家族中假定的磷酸化位点。在基于 10 倍交叉验证和独立数据集的并排比较中，RF-Phos 2.0 优于其他流行的哺乳动物磷酸盐预测方法，例如 PhosphoSVM、GPS2.1 和 Musite。
Protein phosphorylation is one of the most widespread regulatory mechanisms in eukaryotes. Over the past decade, phosphorylation site prediction has emerged as an important problem in the field of bioinformatics. Here, we report a new method, termed Random Forest-based Phosphosite predictor 2.0 (RF-Phos 2.0), to predict phosphorylation sites given only the primary amino acid sequence of a protein as input. RF-Phos 2.0, which uses random forest with sequence and structural features, is able to identify putative sites of phosphorylation across many protein families. In side-by-side comparisons based on 10-fold cross validation and an independent dataset, RF-Phos 2.0 compares favorably to other popular mammalian phosphosite prediction methods, such as PhosphoSVM, GPS2.1, and Musite.