| Summary: | 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.
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