ArticleBioMed research international2015
Prediction of antimicrobial peptides based on sequence alignment and support vector machine-pairwise algorithm utilizing LZ-complexity.
Article in BioMed research international, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Artificial Intelligence-Driven Discovery and Optimization of Antimicrobial Peptides Targeting ESKAPE Pathogens and Multidrug-Resistant Fungi.Microorganisms · 2026Review
- Accelerating the Discovery and Design of Antimicrobial Peptides with Artificial Intelligence.Methods in molecular biology (Clifton, N.J.) · 2024Article
- Krein support vector machine classification of antimicrobial peptides.Digital discovery · 2023Article
- CAMPR4: a database of natural and synthetic antimicrobial peptides.Nucleic acids research · 2023Article
- Emerging Computational Approaches for Antimicrobial Peptide Discovery.Antibiotics (Basel, Switzerland) · 2022Review
- Identification of intelligence-related proteins through a robust two-layer predictor.Communicative & integrative biology · 2022Article
- Machine Learning Prediction of Antimicrobial Peptides.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Machine learning-enabled predictive modeling to precisely identify the antimicrobial peptides.Medical & biological engineering & computing · 2021Article
- Antimicrobial Peptides: An Update on Classifications and Databases.International journal of molecular sciences · 2021Review
- Identification of Distinct Characteristics of Antibiofilm Peptides and Prospection of Diverse Sources for Efficacious Sequences.Frontiers in microbiology · 2021Article
- Collection of antimicrobial peptides database and its derivatives: Applications and beyond.Protein science : a publication of the Protein Society · 2020Article
- ADAPTABLE: a comprehensive web platform of antimicrobial peptides tailored to the user's research.Life science alliance · 2019Article
- De Novo Design and In Vitro Testing of Antimicrobial Peptides against Gram-Negative Bacteria.Pharmaceuticals (Basel, Switzerland) · 2019Article
- Improving protein function prediction using protein sequence and GO-term similarities.Bioinformatics (Oxford, England) · 2019Article
- NeuroPIpred: a tool to predict, design and scan insect neuropeptides.Scientific reports · 2019Article
- PrESOgenesis: A two-layer multi-label predictor for identifying fertility-related proteins using support vector machine and pseudo amino acid composition approach.Scientific reports · 2018Article
- AmPEP: Sequence-based prediction of antimicrobial peptides using distribution patterns of amino acid properties and random forest.Scientific reports · 2018Article
- Empirical comparison of web-based antimicrobial peptide prediction tools.Bioinformatics (Oxford, England) · 2017Article
- Predicting antimicrobial peptides with improved accuracy by incorporating the compositional, physico-chemical and structural features into Chou's general PseAAC.Scientific reports · 2017Article
- Predicting cancerlectins by the optimal g-gap dipeptides.Scientific reports · 2015Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study concerns an attempt to establish a new method for predicting antimicrobial peptides (AMPs) which are important to the immune system. Recently, researchers are interested in designing alternative drugs based on AMPs because they have found that a large number of bacterial strains have become resistant to available antibiotics. However, researchers have encountered obstacles in the AMPs designing process as experiments to extract AMPs from protein sequences are costly and require a long set-up time. Therefore, a computational tool for AMPs prediction is needed to resolve this problem. In this study, an integrated algorithm is newly introduced to predict AMPs by integrating sequence alignment and support vector machine- (SVM-) LZ complexity pairwise algorithm. It was observed that, when all sequences in the training set are used, the sensitivity of the proposed algorithm is 95.28% in jackknife test and 87.59% in independent test, while the sensitivity obtained for jackknife test and independent test is 88.74% and 78.70%, respectively, when only the sequences that has less than 70% similarity are used. Applying the proposed algorithm may allow researchers to effectively predict AMPs from unknown protein peptide sequences with higher sensitivity.
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Registered trials
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