ArticleBMC genomics2020
ACEP: improving antimicrobial peptides recognition through automatic feature fusion and amino acid embedding.
Article in BMC genomics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed, 57 citations in OpenAlex.
- Advances in deep learning for antimicrobial research.Frontiers in microbiology · 2026Review
- Harnessing Machine Learning Approaches for the Identification, Characterization, and Optimization of Novel Antimicrobial Peptides.Antibiotics (Basel, Switzerland) · 2025Review
- A Comprehensive Overview of Antimicrobial Peptides: Broad-Spectrum Activity, Computational Approaches, and Applications.Antibiotics (Basel, Switzerland) · 2025Review
- In Silico Discovery and Sensory Validation of Umami Peptides in Fermented Sausages: A Study Integrating Deep Learning and Molecular Modeling.Foods (Basel, Switzerland) · 2025Article
- Multimodal geometric learning for antimicrobial peptide identification by leveraging alphafold2-predicted structures and surface features.Briefings in bioinformatics · 2025Article
- BERT-AmPEP60: A BERT-Based Transfer Learning Approach to Predict the Minimum Inhibitory Concentrations of Antimicrobial Peptides forJournal of chemical information and modeling · 2025Article
- Deep-Learning-Based Approaches for Rational Design of Stapled Peptides With High Antimicrobial Activity and Stability.Microbial biotechnology · 2025Article
- Article
- Computational Prediction and Structural Analysis of α-Hairpinins, a Ubiquitous Family of Antimicrobial Peptides, Using the Cysmotif Searcher Pipeline.Antibiotics (Basel, Switzerland) · 2024Article
- Screening antimicrobial peptides and probiotics using multiple deep learning and directed evolution strategies.Acta pharmaceutica Sinica. B · 2024Article
- Development of a logic regression-based approach for the discovery of host- and niche-informative biomarkers inApplied and environmental microbiology · 2024Article
- An active machine learning discovery platform for membrane-disrupting and pore-forming peptides.Physical chemistry chemical physics : PCCP · 2024Article
- Examining evolutionary scale modeling-derived different-dimensional embeddings in the antimicrobial peptide classification through a KNIME workflow.Protein science : a publication of the Protein Society · 2024Article
- AntiBP3: A Method for Predicting Antibacterial Peptides against Gram-Positive/Negative/Variable Bacteria.Antibiotics (Basel, Switzerland) · 2024Article
- Machine learning tools for peptide bioactivity evaluation - Implications for cell culture media optimization and the broader cultivated meat industry.Current research in food science · 2024Article
- iAMPCN: a deep-learning approach for identifying antimicrobial peptides and their functional activities.Briefings in bioinformatics · 2023Article
- Rational design of stapled antimicrobial peptides.Amino acids · 2023Review
- TriNet: A tri-fusion neural network for the prediction of anticancer and antimicrobial peptides.Patterns (New York, N.Y.) · 2023Article
- Recent Progress in the Discovery and Design of Antimicrobial Peptides Using Traditional Machine Learning and Deep Learning.Antibiotics (Basel, Switzerland) · 2022Review
- Machine Learning Prediction of Antimicrobial Peptides.Methods in molecular biology (Clifton, N.J.) · 2022Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAntimicrobial resistance is one of our most serious health threats. Antimicrobial peptides (AMPs), effecter molecules of innate immune system, can defend host organisms against microbes and most have shown a lowered likelihood for bacteria to form resistance compared to many conventional drugs. Thus, AMPs are gaining popularity as better substitute to antibiotics. To aid researchers in novel AMPs discovery, we design computational approaches to screen promising candidates.
resultsIn this work, we design a deep learning model that can learn amino acid embedding patterns, automatically extract sequence features, and fuse heterogeneous information. Results show that the proposed model outperforms state-of-the-art methods on recognition of AMPs. By visualizing data in some layers of the model, we overcome the black-box nature of deep learning, explain the working mechanism of the model, and find some import motifs in sequences.
conclusionsACEP model can capture similarity between amino acids, calculate attention scores for different parts of a peptide sequence in order to spot important parts that significantly contribute to final predictions, and automatically fuse a variety of heterogeneous information or features. For high-throughput AMPs recognition, open source software and datasets are made freely available at https://github.com/Fuhaoyi/ACEP .
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.