Ubiquitin is an important type of protein after translational modification. Ubiquitin has the ability to take part in several cellular regulations among several biological processions. At the same time, ubiquitin plays key roles in the enzymatic process. So as to construct the new tool to classify the ubiquitin amino acid residues, we employed the random forest model to classify the ubiquitin sites utilizing the experimentally identified ubiquitinated protein sequences of A. thaliana. More detailed, we utilized the k-spaced amino acid pair (CKSAAP) encoding and binary encoding to deal with the potential protein segments. The proposed tools can obtain 72.83% in Sp, 72.42% in Sn, 72.63% in Acc, and 0.4525 in MCC. With these performances, such tools can obtain the available results in the dataset of Arabidopsis.

中文翻译：

---

模型植物拟南芥中蛋白质泛素位点的预测和分析

泛素是翻译修饰后的重要蛋白质类型。泛素具有参与多个生物游行中的多个细胞调节的能力。同时，泛素在酶促过程中起关键作用。为了构建分类泛素氨基酸残基的新工具，我们利用随机森林模型利用实验鉴定的拟南芥泛素化蛋白质序列对泛素位点进行分类。更详细的，我们使用的ķ等距氨基酸对（CKSAAP）编码和二进制编码以处理潜在的蛋白质片段。所提出的工具可以获得Sp的72.83％，Sn的72.42％，Acc的72.63％和MCC的0.4525。借助这些性能，此类工具可以获得拟南芥数据集中的可用结果。