N-gram Features and Machine Learning Models for Recognizing Human Enzyme Protein Sequences

The identification of enzyme proteins is crucial for understanding the underlying mechanisms of various biological processes. In recent years, the development of machine learning techniques has made it possible to automate this task with high accuracy. In this study, we propose a method that uses 3-gram features and machine learning models for recognizing human enzyme protein sequences. First, the 3-gram features are extracted from the amino acid sequences of enzymes and use these features as input to several machine learning models, including naive bayes, decision tree, random forest, and Extra tree (ET). We compare the performance of these models using metrics such as accuracy, precision, recall, and F1 score. The results obtained show that the ET model performs the best, achieving an accuracy of 96%, precision of 93%, recall of 93%, and F1 score of 93%. Overall, the proposed approach demonstrates the potential of using machine learning models and 3-gram features for accurate identification of human enzyme protein sequences.