Abstract
Protein sequence classification is vital for understanding protein functionalities, aiding in the inference of novel protein functions. Machine learning and deep learning algorithms have revolutionized this field, offering insights into specific protein classes and functions. This study employs Natural Language Processing (NLP) techniques, including Integer and Blosum encoding, for efficient classification. SVM with count vectorizer achieves the highest accuracy of 92%, while Integer encoding with CNN surpasses NLP embedding techniques by 4%. The goal is to develop an automated system for predicting protein functionality based on sequence classification, contributing to advancements in proteomics and computational biology.
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