Deepenz: prediction of enzyme classification by deep learning - Hamza Chehili; Salah Eddine Aliouane; Abdelhafedh Bendahmane; Mohamed Abdelhafid Hamidechi (Institute of Advanced Engineering and Science, 2021)
Previously, the classification of enzymes was carried out by traditional heuritic methods, however, due to the rapid increase in the number of enzymes being discovered, new methods aimed to classify them are required. Their goal is to increase the speed of processing and to improve the accuracy of predictions. The Purpose of this work is to develop an approach that predicts the enzymes’ classification. This approach is based on two axes of artificial intelligence (AI): natural language processing (NLP) and deep learning (DL). The results obtained in the tests show the effectiveness of this approach. The combination of these two tools give a model with a great capacity to extract knowledge from enzyme data to predict and classify them. The proposed model learns through intensive training by exploiting enzyme sequences. This work highlights the contribution of this approach to improve the precision of enzyme classification.