ArticleBioinformatics (Oxford, England)2022
Integrating transformer and imbalanced multi-label learning to identify antimicrobial peptides and their functional activities.
Article in Bioinformatics (Oxford, England), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed.
- Artificial Intelligence-Driven Antimicrobial Peptide Discovery: Prediction, Generation, Mining and Optimization.Probiotics and antimicrobial proteins · 2026Review
- miRSiC: a regulatory-aware machine learning framework for microRNA expression inference across bulk and single-cell transcriptomes.Briefings in bioinformatics · 2026Article
- PepPharmaHub: a cloud-based platform integrating multimodel language architectures with curated data resources for therapeutic peptide discovery.BMC biology · 2026Article
- MAPLE: interpretable deep learning identifies selective antimicrobial peptides using joint evolutionary-physicochemical analysis.Briefings in bioinformatics · 2026Article
- Lab to codes: multimodel advanced AI-driven pipelines to drive antimicrobial peptide design for the post-antibiotic era.Frontiers in cellular and infection microbiology · 2026Review
- Article
- Advances in deep learning for antimicrobial research.Frontiers in microbiology · 2026Review
- Prediction of Chemically Modified Antimicrobial Peptides and Their Sub-functional Activities Using Hybrid Features.Probiotics and antimicrobial proteins · 2026Article
- tsAMP: a strain-level antimicrobial peptide identification framework based on large language models and pathogen genomic variation.Frontiers in microbiology · 2026Article
- Harnessing Machine Learning Approaches for the Identification, Characterization, and Optimization of Novel Antimicrobial Peptides.Antibiotics (Basel, Switzerland) · 2025Review
- Transfer learning on protein language models improves antimicrobial peptide classification.Scientific reports · 2025Article
- Pepxml: ESM2-based extreme multilabel classification of pathogen-targeted antimicrobial peptides.Briefings in bioinformatics · 2025Article
- 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
- AEPMA: peptide-microbe association prediction based on autoevolutionary heterogeneous graph learning.Briefings in bioinformatics · 2025Article
- Article
- AI-Driven Antimicrobial Peptide Discovery: Mining and Generation.Accounts of chemical research · 2025Review
- Exploring the repository of de novo-designed bifunctional antimicrobial peptides through deep learning.eLife · 2025Article
- Leveraging large language models for peptide antibiotic design.Cell reports. Physical science · 2025Article
- AI Methods for Antimicrobial Peptides: Progress and Challenges.Microbial biotechnology · 2025Review
- HemoFuse: multi-feature fusion based on multi-head cross-attention for identification of hemolytic peptides.Scientific reports · 2024Article
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5 authors.
Funding
Abstract
motivationAntimicrobial peptides (AMPs) have the potential to inhibit multiple types of pathogens and to heal infections. Computational strategies can assist in characterizing novel AMPs from proteome or collections of synthetic sequences and discovering their functional abilities toward different microbial targets without intensive labor.
resultsHere, we present a deep learning-based method for computer-aided novel AMP discovery that utilizes the transformer neural network architecture with knowledge from natural language processing to extract peptide sequence information. We implemented the method for two AMP-related tasks: the first is to discriminate AMPs from other peptides, and the second task is identifying AMPs functional activities related to seven different targets (gram-negative bacteria, gram-positive bacteria, fungi, viruses, cancer cells, parasites and mammalian cell inhibition), which is a multi-label problem. In addition, asymmetric loss was adopted to resolve the intrinsic imbalance of dataset, particularly for the multi-label scenarios. The evaluation showed that our proposed scheme achieves the best performance for the first task (96.85% balanced accuracy) and has a more unbiased prediction for the second task (79.83% balanced accuracy averaged across all functional activities) when compared with that of strategies without imbalanced learning or deep learning. AVAILABILITY AND IMPLEMENTATION: The source code and data of this study are available at https://github.com/BiOmicsLab/TransImbAMP. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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