Enzyme Classification: Unveiling Insights Through Exploratory Data Analysis and DenseNet201 CNN Modelling

With the integration of a DenseNet201 CNN model with exploratory data analysis (EDA), this study presents a access to comprehensive method for enzyme classification. Using a dataset of 858,777 amino acid sequences that have been labeled, the model is able to predict enzyme classes in a test set of 253,146 samples. However, it excludes sequences that are longer than a certain length. Preprocessing entails excluding infrequent amino acids (X, U, B, Z) and considering amino acids (B, Z) that are only present in the training set. The DenseNet201 convolutional neural network (CNN) architecture, which includes an embedding layer, bidirectional LSTM layers, and a dense output layer, achieves a favorable accuracy of 79% on the test set after 20 epochs. The model's effectiveness across 20 enzyme classes is demonstrated by a comprehensive categorization report and confusion matrix. This study enhances social enzyme classification innovation approaches, showcasing the importance of integrating exploratory data analysis (EDA) with neural networks (NN) in the fields of bioinformatics and molecular biology. Future efforts entail investigating supplementary characteristics and optimization techniques to further refine the precision of the model.