ArticleComputational and structural biotechnology journal2020
An automatic representation of peptides for effective antimicrobial activity classification.
Article in Computational and structural biotechnology journal, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 15 citations in OpenAlex.
- Design of Natterins-based peptides improves antimicrobial and antiviral activities.Biotechnology reports (Amsterdam, Netherlands) · 2025Article
- AI Methods for Antimicrobial Peptides: Progress and Challenges.Microbial biotechnology · 2025Review
- Exploring the modulatory impact of isosakuranetin onVirulence · 2023Article
- Embedded-AMP: A Multi-Thread Computational Method for the Systematic Identification of Antimicrobial Peptides Embedded in Proteome Sequences.Antibiotics (Basel, Switzerland) · 2023Article
- Antimicrobial peptides: features, applications and the potential use against covid-19.Molecular biology reports · 2022Review
- Antimicrobial peptides with cell-penetrating activity as prophylactic and treatment drugs.Bioscience reports · 2022Review
- Using an Ensemble to Identify and Classify Macroalgae Antimicrobial Peptides.Interdisciplinary sciences, computational life sciences · 2021Article
- Natural bacterial isolates as an inexhaustible source of new bacteriocins.Applied microbiology and biotechnology · 2021Review
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Antimicrobial peptides (AMPs) are a promising alternative to small-molecules-based antibiotics. These peptides are part of most living organisms' innate defense system. In order to computationally identify new AMPs within the peptides these organisms produce, an automatic AMP/non-AMP classifier is required. In order to have an efficient classifier, a set of robust features that can capture what differentiates an AMP from another that is not, has to be selected. However, the number of candidate descriptors is large (in the order of thousands) to allow for an exhaustive search of all possible combinations. Therefore, efficient and effective feature selection techniques are required. In this work, we propose an efficient wrapper technique to solve the feature selection problem for AMPs identification. The method is based on a Genetic Algorithm that uses a variable-length chromosome for representing the selected features and uses an objective function that considers the Mathew Correlation Coefficient and the number of selected features. Computational experiments show that the proposed method can produce competitive results regarding sensitivity, specificity, and MCC. Furthermore, the best classification results are achieved by using only 39 out of 272 molecular descriptors.
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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.