ArticleThe Journal of membrane biology2016
Classifying Multifunctional Enzymes by Incorporating Three Different Models into Chou's General Pseudo Amino Acid Composition.
Article in The Journal of membrane biology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Accurately predicting enzyme functions through geometric graph learning on ESMFold-predicted structures.Nature communications · 2024Article
- iTTCA-MFF: identifying tumor T cell antigens based on multiple feature fusion.Immunogenetics · 2022Article
- A hierarchical deep learning based approach for multi-functional enzyme classification.Protein science : a publication of the Protein Society · 2021Article
- Machine learning for enzyme engineering, selection and design.Protein engineering, design & selection : PEDS · 2021Review
- Some illuminating remarks on molecular genetics and genomics as well as drug development.Molecular genetics and genomics : MGG · 2020Review
- Using Random Forest Model Combined With Gabor Feature to Predict Protein-Protein Interaction From Protein Sequence.Evolutionary bioinformatics online · 2020Article
- DEEPre: sequence-based enzyme EC number prediction by deep learning.Bioinformatics (Oxford, England) · 2018Article
- mlDEEPre: Multi-Functional Enzyme Function Prediction With Hierarchical Multi-Label Deep Learning.Frontiers in genetics · 2018Article
- Article
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2 authors.
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Abstract
With the avalanche of the newly found protein sequences in the post-genomic epoch, there is an increasing trend for annotating a number of newly discovered enzyme sequences. Among the various proteins, enzyme was considered as the one of the largest kind of proteins. It takes part in most of the biochemical reactions and plays a key role in metabolic pathways. Multifunctional enzyme is enzyme that plays multiple physiological roles. Given a multifunctional enzyme sequence, how can we identify its class? Especially, how can we deal with the multi-classes problem since an enzyme may simultaneously belong to two or more functional classes? To address these problems, which are obviously very important both to basic research and drug development, a multi-label classifier was developed via three different prediction models with multi-label K-nearest algorithm. Experimental results obtained on a stringent benchmark dataset of enzymes by jackknife cross-validation test show that the predicting results were exciting, indicating that the current method could be an effective and promising high throughput method in the enzyme research. We hope it could play an important complementary role to the existing predictors in identifying the classes of enzymes.
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