All published articles of this journal are available on ScienceDirect.
EZYDeep: A Deep Learning Tool for Enzyme Function Prediction based on Sequence Information
Salah Eddine Aliouane2
Hamza Chehili2
Mohamed Skander Daas2
Adel Belbekri3
Mohamed Abdelhafid Hamidechi2
Authors Info & Affiliations
Abstract
Introduction:
Enzymes play a crucial role in numerous chemical processes that are essential for life. Accurate prediction and classification of enzymes are crucial for bioindustrial and biomedical applications.
Methods:
In this study, we present EZYDeep, a deep learning tool based on convolutional neural networks, for classifying enzymes based on their sequence information. The tool was evaluated against two existing methods, HECNet and DEEPre, on the HECNet July 2019 dataset, and showed exceptional performance with accuracy rates over 95% at all four levels of prediction.
Results:
Additionally, our tool was compared to state-of-the-art enzyme function prediction tools and demonstrated superior performance at all levels of prediction. We also developed a user-friendly web application for the tool, making it easily accessible to researchers and practitioners.
Conclusion:
Our work demonstrates the potential of using machine learning techniques for accurate and efficient enzyme classification, highlighting the significance of sequence information in predicting enzyme function.
