ArticleProtein science : a publication of the Protein Society2021
A hierarchical deep learning based approach for multi-functional enzyme classification.
Article in Protein science : a publication of the Protein Society, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Oxidoreductase-Like Nanozymes: From Biosensing to Molecular Mechanisms in Disease Therapy.International journal of nanomedicine · 2026Review
- Prediction of enzyme function using an interpretable optimized ensemble learning framework.Chemical science · 2025Article
- Mining versatile feruloyl esterases: phylogenetic classification, structural features, and deep learning model.Bioresources and bioprocessing · 2025Article
- Identification and validation of key miRNAs and a microRNA-mRNA regulatory network associated with liver cancer.Cell cycle (Georgetown, Tex.) · 2024Article
- A Comprehensive Review of the Diversity of Fungal Secondary Metabolites and Their Emerging Applications in Healthcare and Environment.Mycobiology · 2024Review
- EnzymeNet: residual neural networks model for Enzyme Commission number prediction.Bioinformatics advances · 2023Article
- A hierarchical deep learning based approach for multi-functional enzyme classification.Protein science : a publication of the Protein Society · 2021Article
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3 authors.
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Abstract
Enzymes are critical proteins in every organism. They speed up essential chemical reactions, help fight diseases, and have a wide use in the pharmaceutical and manufacturing industries. Wet lab experiments to figure out an enzyme's function are time consuming and expensive. Therefore, the need for computational approaches to address this problem are becoming necessary. Usually, an enzyme is extremely specific in performing its function. However, there exist enzymes that can perform multiple functions. A multi-functional enzyme has vast potential as it reduces the need to discover/use different enzymes for different functions. We propose an approach to predict a multi-functional enzyme's function up to the most specific fourth level of the hierarchy of the Enzyme Commission (EC) number. Previous studies can only predict the function of the enzyme till level 1. Using a dataset of 2,583 multi-functional enzymes, we achieved a hierarchical subset accuracy of 71.4% and a Macro F
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