ArticleBMC bioinformatics2019
An advanced approach to identify antimicrobial peptides and their function types for penaeus through machine learning strategies.
Article in BMC bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it, 62 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
- Marine Antimicrobial Peptides: From Ocean Biodiversity to Genome Mining, Multi-Omics Discovery, and Biotechnological Innovation in the Battle Against Antimicrobial Resistance.Probiotics and antimicrobial proteins · 2026Review
- 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
- Article
- Multi-label classification and features investigation of antimicrobial peptides with various functional classes.iScience · 2023Article
- Advances in Antimicrobial Peptide Discovery via Machine Learning and Delivery via Nanotechnology.Microorganisms · 2023Review
- Emerging Computational Approaches for Antimicrobial Peptide Discovery.Antibiotics (Basel, Switzerland) · 2022Review
- Assessing sequence-based protein-protein interaction predictors for use in therapeutic peptide engineering.Scientific reports · 2022Article
- A review on antimicrobial peptides databases and the computational tools.Database : the journal of biological databases and curation · 2022Review
- Co-AMPpred for in silico-aided predictions of antimicrobial peptides by integrating composition-based features.BMC bioinformatics · 2021Article
- Relevant Features of Polypharmacologic Human-Target Antimicrobials Discovered by Machine-Learning Techniques.Pharmaceuticals (Basel, Switzerland) · 2020Article
- Article
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAntimicrobial peptides (AMPs) are essential components of the innate immune system and can protect the host from various pathogenic bacteria. The marine environment is known to be one of the richest sources for AMPs. Effective usage of AMPs and their derivatives can greatly improve the immunity and breeding survival rate of aquatic products. It is highly desirable to develop computational tools for rapidly and accurately identifying AMPs and their functional types, for the purpose of helping design new and more effective antimicrobial agents.
resultsIn this study, we made an attempt to develop an advanced machine learning based computational approach, MAMPs-Pred, for identification of AMPs and its function types. Initially, SVM-prot 188-D features were extracted that were subsequently used as input to a two-layer multi-label classifier. In specific, the first layer is to identify whether it is an AMP by applying RF classifier, and the second layer addresses the multi-type problem by identifying the activites or function types of AMPs by applying PS-RF and LC-RF classifiers. To benchmark the methods,the MAMPs-Pred method is also compared with existing best-performing methods in literature and has shown an improved identification accuracy.
conclusionsThe results reported in this study indicate that the MAMP-Pred method achieves high performance for identifying AMPs and its functional types.The proposed approach is believed to supplement the tools and techniques that have been developed in the past for predicting AMPs and their function types.
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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.