ArticleFrontiers in genetics2018
mlDEEPre: Multi-Functional Enzyme Function Prediction With Hierarchical Multi-Label Deep Learning.
Article in Frontiers in genetics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.
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Who cites it
37 citing papers in PubMed.
- Large language models enhance annotation of enzymes in metagenomes.Science advances · 2026Article
- AI-Driven Enzyme Engineering: Emerging Models and Next-Generation Biotechnological Applications.Molecules (Basel, Switzerland) · 2025Review
- BPFun: a deep learning framework for bioactive peptide function prediction using multi-label strategy by transformer-driven and sequence rich intrinsic information.BMC bioinformatics · 2025Article
- RC-GNN: A predictive model of enzyme-reaction pairs.bioRxiv : the preprint server for biology · 2025Article
- Learning maximally spanning representations improves protein function annotation.bioRxiv : the preprint server for biology · 2025Article
- Deep learning in template-free de novo biosynthetic pathway design of natural products.Briefings in bioinformatics · 2024Review
- Insights into the inner workings of transformer models for protein function prediction.Bioinformatics (Oxford, England) · 2024Article
- PredictEFC: a fast and efficient multi-label classifier for predicting enzyme family classes.BMC bioinformatics · 2024Article
- Precision enzyme discovery through targeted mining of metagenomic data.Natural products and bioprospecting · 2024Review
- Enzyme function and evolution through the lens of bioinformatics.The Biochemical journal · 2023Review
- Enzyme Databases in the Era of Omics and Artificial Intelligence.International journal of molecular sciences · 2023Review
- An explainability framework for deep learning on chemical reactions exemplified by enzyme-catalysed reaction classification.Journal of cheminformatics · 2023Article
- AcrNET: predicting anti-CRISPR with deep learning.Bioinformatics (Oxford, England) · 2023Article
- MAHOMES II: A webserver for predicting if a metal binding site is enzymatic.Protein science : a publication of the Protein Society · 2023Article
- MAHOMES II: A webserver for predicting if a metal binding site is enzymatic.bioRxiv : the preprint server for biology · 2023Article
- EnzymeNet: residual neural networks model for Enzyme Commission number prediction.Bioinformatics advances · 2023Article
- Effects of Sequence Features on Machine-Learned Enzyme Classification Fidelity.Biochemical engineering journal · 2022Article
- Deep embeddings to comprehend and visualize microbiome protein space.Scientific reports · 2022Article
- DeeReCT-APA: Prediction of Alternative Polyadenylation Site Usage Through Deep Learning.Genomics, proteomics & bioinformatics · 2022Article
- Current progress and open challenges for applying deep learning across the biosciences.Nature communications · 2022Review
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4 authors.
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Abstract
As a great challenge in bioinformatics, enzyme function prediction is a significant step toward designing novel enzymes and diagnosing enzyme-related diseases. Existing studies mainly focus on the mono-functional enzyme function prediction. However, the number of multi-functional enzymes is growing rapidly, which requires novel computational methods to be developed. In this paper, following our previous work, DEEPre, which uses deep learning to annotate mono-functional enzyme's function, we propose a novel method, mlDEEPre, which is designed specifically for predicting the functionalities of multi-functional enzymes. By adopting a novel loss function, associated with the relationship between different labels, and a self-adapted label assigning threshold, mlDEEPre can accurately and efficiently perform multi-functional enzyme prediction. Extensive experiments also show that mlDEEPre can outperform the other methods in predicting whether an enzyme is a mono-functional or a multi-functional enzyme (mono-functional vs. multi-functional), as well as the main class prediction across different criteria. Furthermore, due to the flexibility of mlDEEPre and DEEPre, mlDEEPre can be incorporated into DEEPre seamlessly, which enables the updated DEEPre to handle both mono-functional and multi-functional predictions without human intervention.
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