Identification of SARS-CoV-2 B-cell epitope with fuzzy decision tree model.

This study aimed to identify B-cell epitopes of SARS-CoV-2 virus through the use of fuzzy decision trees. This method was chosen because of its ability to handle ambiguous or uncertain data, which is crucial in studying the genetic makeup of the virus that is prone to change. The study used a dataset of secondary data containing information about protein sequences and initial positions. The data was preprocessed by using only the first 2000 data, dividing it into 1400 training data and 600 test data, and removing some features. The fuzzy decision tree model was generated from the training data using the fuzzy tree library in python. The model was evaluated using several metrics, including accuracy, precision, recall, f1 score, and confusion matrix. The results showed that the fuzzy decision tree model was successful in identifying the B-cell epitopes of SARS-CoV-2, providing valuable information for the development of effective COVID-19 vaccines. [ABSTRACT FROM AUTHOR]