ArticleBriefings in bioinformatics2022
Comparative analysis of machine learning algorithms on the microbial strain-specific AMP prediction.
Article in Briefings in bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- Artificial intelligence catalyzes antimicrobial peptide design.Synthetic and systems biotechnology · 2027Review
- Harnessing snake venom cardiotoxins for antimicrobial peptide discovery.npj drug discovery · 2026Article
- Lipidomic Profile Reconstruction of Therapeutic Membrane Targets Using Physics-Based Optimization with Limited Activity Data.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- MAPLE: interpretable deep learning identifies selective antimicrobial peptides using joint evolutionary-physicochemical analysis.Briefings in bioinformatics · 2026Article
- De Novo Multi-Mechanism Antimicrobial Peptide Design via Multimodal Deep Learning.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Focusing on the Unfocused: Prominence of Proteo-transcriptomic Studies on Venomous Sea Snakes.Biochemical genetics · 2026Review
- SGAC: a graph neural network framework for imbalanced and structure-aware AMP classification.Briefings in bioinformatics · 2026Article
- Hybrid and conjugated antimicrobial peptides: new tactics to counter bacterial resistance.Frontiers in microbiology · 2026Review
- Impact of Ultrasound Pretreatment and Enzyme Concentration on Taste and Biological Activities of Porcine Lung Hydrolyzates.Foods (Basel, Switzerland) · 2025Article
- Structural information in therapeutic peptides: Emerging applications in biomedicine.FEBS open bio · 2025Review
- Unlocking Antimicrobial Peptides: In Silico Proteolysis and Artificial Intelligence-Driven Discovery from Cnidarian Omics.Molecules (Basel, Switzerland) · 2025Article
- An ensemble deep learning model for predicting minimum inhibitory concentrations of antimicrobial peptides against pathogenic bacteria.iScience · 2024Article
- Recent approaches in the application of antimicrobial peptides in food preservation.World journal of microbiology & biotechnology · 2024Review
- Identification, Synthesis, and In Vitro Activities of Antimicrobial Peptide from African Catfish against the Extended-Spectrum Beta-Lactamase (ESBL)-ProducingPharmaceutics · 2024Article
- Tackling the Antimicrobial Resistance "Pandemic" with Machine Learning Tools: A Summary of Available Evidence.Microorganisms · 2024Review
- Development of sensor system and data analytic framework for non-invasive blood glucose prediction.Scientific reports · 2024Article
- Article
- Contrastive learning for enhancing feature extraction in anticancer peptides.Briefings in bioinformatics · 2024Article
- Article
- Palm Multidiagnostic ofTransboundary and emerging diseases · 2024Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The evolution of drug-resistant pathogenic microbial species is a major global health concern. Naturally occurring, antimicrobial peptides (AMPs) are considered promising candidates to address antibiotic resistance problems. A variety of computational methods have been developed to accurately predict AMPs. The majority of such methods are not microbial strain specific (MSS): they can predict whether a given peptide is active against some microbe, but cannot accurately calculate whether such peptide would be active against a particular MS. Due to insufficient data on most MS, only a few MSS predictive models have been developed so far. To overcome this problem, we developed a novel approach that allows to improve MSS predictive models (MSSPM), based on properties, computed for AMP sequences and characteristics of genomes, computed for target MS. New models can perform predictions of AMPs for MS that do not have data on peptides tested on them. We tested various types of feature engineering as well as different machine learning (ML) algorithms to compare the predictive abilities of resulting models. Among the ML algorithms, Random Forest and AdaBoost performed best. By using genome characteristics as additional features, the performance for all models increased relative to models relying on AMP sequence-based properties only. Our novel MSS AMP predictor is freely accessible as part of DBAASP database resource at http://dbaasp.org/prediction/genome.
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