ArticleBioinformatics (Oxford, England)2019
Identifying antimicrobial peptides using word embedding with deep recurrent neural networks.
Article in Bioinformatics (Oxford, England), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 57 papers, 1 of them a synthesis that pooled it.
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Who cites it
57 citing papers in PubMed, 1 synthesis or guideline pooled it, 143 citations in OpenAlex.
- Protein Sequence Analysis landscape: A Systematic Review of Task Types, Databases, Datasets, Word Embeddings Methods, and Language Models.Database : the journal of biological databases and curation · 2025Pooled it
- Artificial Intelligence-Driven Antimicrobial Peptide Discovery: Prediction, Generation, Mining and Optimization.Probiotics and antimicrobial proteins · 2026Review
- Antimicrobial peptides: An important link in the game theory between plants and pathogens.Journal of advanced research · 2026Review
- NeuroPpred-MSN: A Neuropeptide Prediction Model Based on Multi-feature Fusion and Siamese Networks.Interdisciplinary sciences, computational life sciences · 2026Article
- PepGraphormer: an ESM-GAT hybrid deep learning framework for antimicrobial peptide prediction.Journal of cheminformatics · 2026Article
- Advances in deep learning for antimicrobial research.Frontiers in microbiology · 2026Review
- Actifensin Evolution in the Human Oral Cavity over the Past 100,000 Years.Journal of the American Chemical Society · 2025Article
- iEnhancer-GDM: A Deep Learning Framework Based on Generative Adversarial Network and Multi-head Attention Mechanism to Identify Enhancers and Their Strength.Interdisciplinary sciences, computational life sciences · 2025Article
- Review
- Antibacterial microcins are ubiquitous and functionally diverse across bacterial communities.Nature communications · 2025Article
- The potential of bacteriocins and bacteriophages to control bacterial disease of crops with a focus onJournal of the Royal Society of New Zealand · 2025Review
- Bacteriocin prediction through cross-validation-based and hypergraph-based feature evaluation approaches.Frontiers in bioinformatics · 2025Article
- AI Methods for Antimicrobial Peptides: Progress and Challenges.Microbial biotechnology · 2025Review
- Protein remote homology detection and structural alignment using deep learning.Nature biotechnology · 2024Article
- Improvements in viral gene annotation using large language models and soft alignments.BMC bioinformatics · 2024Article
- Accelerating the Discovery and Design of Antimicrobial Peptides with Artificial Intelligence.Methods in molecular biology (Clifton, N.J.) · 2024Article
- Effects of Isalo scorpion cytotoxic peptide on growth, immune performance, and intestinal flora of yellow-feathered broilers.Frontiers in veterinary science · 2024Article
- Deep learning neural network development for the classification of bacteriocin sequences produced by lactic acid bacteria.F1000Research · 2024Article
- Discovering genomic islands in unannotated bacterial genomes using sequence embedding.Bioinformatics advances · 2024Article
- PorcineAI-Enhancer: Prediction of Pig Enhancer Sequences Using Convolutional Neural Networks.Animals : an open access journal from MDPI · 2023Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
motivationAntibiotic resistance constitutes a major public health crisis, and finding new sources of antimicrobial drugs is crucial to solving it. Bacteriocins, which are bacterially produced antimicrobial peptide products, are candidates for broadening the available choices of antimicrobials. However, the discovery of new bacteriocins by genomic mining is hampered by their sequences' low complexity and high variance, which frustrates sequence similarity-based searches.
resultsHere we use word embeddings of protein sequences to represent bacteriocins, and apply a word embedding method that accounts for amino acid order in protein sequences, to predict novel bacteriocins from protein sequences without using sequence similarity. Our method predicts, with a high probability, six yet unknown putative bacteriocins in Lactobacillus. Generalized, the representation of sequences with word embeddings preserving sequence order information can be applied to peptide and protein classification problems for which sequence similarity cannot be used. AVAILABILITY AND IMPLEMENTATION: Data and source code for this project are freely available at: https://github.com/nafizh/NeuBI. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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Registered trials
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.