ArticleBioinformatics (Oxford, England)2016
Imbalanced multi-label learning for identifying antimicrobial peptides and their functional types.
Article in Bioinformatics (Oxford, England), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers, 1 of them a synthesis that pooled it.
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Who cites it
43 citing papers in PubMed, 1 synthesis or guideline pooled it.
- 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
- SA-MTP: a structure-aware framework for multifunctional therapeutic peptide annotation.Briefings in bioinformatics · 2026Article
- MAPLE: interpretable deep learning identifies selective antimicrobial peptides using joint evolutionary-physicochemical analysis.Briefings in bioinformatics · 2026Article
- Leveraging Different Distance Functions to Predict Antiviral Peptides with Geometric Deep Learning from ESMFold-Predicted Tertiary Structures.Antibiotics (Basel, Switzerland) · 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
- Harnessing Machine Learning Approaches for the Identification, Characterization, and Optimization of Novel Antimicrobial Peptides.Antibiotics (Basel, Switzerland) · 2025Review
- AMHF-TP: Multifunctional therapeutic peptides prediction based on multi-granularity hierarchical features.Quantitative biology (Beijing, China) · 2025Article
- PROTA: A Robust Tool for Protamine Prediction Using a Hybrid Approach of Machine Learning and Deep Learning.International journal of molecular sciences · 2024Article
- Protein Language Models and Machine Learning Facilitate the Identification of Antimicrobial Peptides.International journal of molecular sciences · 2024Article
- Tackling the Antimicrobial Resistance "Pandemic" with Machine Learning Tools: A Summary of Available Evidence.Microorganisms · 2024Review
- Diff-AMP: tailored designed antimicrobial peptide framework with all-in-one generation, identification, prediction and optimization.Briefings in bioinformatics · 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
- Accelerating the Discovery and Design of Antimicrobial Peptides with Artificial Intelligence.Methods in molecular biology (Clifton, N.J.) · 2024Article
- iAMP-Attenpred: a novel antimicrobial peptide predictor based on BERT feature extraction method and CNN-BiLSTM-Attention combination model.Briefings in bioinformatics · 2023Article
- Article
- iAMPCN: a deep-learning approach for identifying antimicrobial peptides and their functional activities.Briefings in bioinformatics · 2023Article
- Integrating transformer and imbalanced multi-label learning to identify antimicrobial peptides and their functional activities.Bioinformatics (Oxford, England) · 2022Article
- ABP-Finder: A Tool to Identify Antibacterial Peptides and the Gram-Staining Type of Targeted Bacteria.Antibiotics (Basel, Switzerland) · 2022Article
- Emerging Computational Approaches for Antimicrobial Peptide Discovery.Antibiotics (Basel, Switzerland) · 2022Review
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
motivationWith the rapid increase of infection resistance to antibiotics, it is urgent to find novel infection therapeutics. In recent years, antimicrobial peptides (AMPs) have been utilized as potential alternatives for infection therapeutics. AMPs are key components of the innate immune system and can protect the host from various pathogenic bacteria. Identifying AMPs and their functional types has led to many studies, and various predictors using machine learning have been developed. However, there is room for improvement; in particular, no predictor takes into account the lack of balance among different functional AMPs.
resultsIn this paper, a new synthetic minority over-sampling technique on imbalanced and multi-label datasets, referred to as ML-SMOTE, was designed for processing and identifying AMPs' functional families. A novel multi-label classifier, MLAMP, was also developed using ML-SMOTE and grey pseudo amino acid composition. The classifier obtained 0.4846 subset accuracy and 0.16 hamming loss. AVAILABILITY AND IMPLEMENTATION: A user-friendly web-server for MLAMP was established at http://www.jci-bioinfo.cn/MLAMP CONTACTS: linweizhong@jci.edu.cn or xudong@missouri.edu.
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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.