ReviewEXCLI journal2022
Recent development of machine learning-based methods for the prediction of defensin family and subfamily.
Review in EXCLI journal, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Selective α-Amylase Inhibition by Plant Defensins: Structural Determinants, Engineering Strategies, and Translational Prospects.Probiotics and antimicrobial proteins · 2026Review
- Integrating evolutionary and compositional features with ML and DL for robust and interpretable druggable protein prediction.Journal of computer-aided molecular design · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Nearly all living species comprise of host defense peptides called defensins, that are crucial for innate immunity. These peptides work by activating the immune system which kills the microbes directly or indirectly, thus providing protection to the host. Thus far, numerous preclinical and clinical trials for peptide-based drugs are currently being evaluated. Although, experimental methods can help to precisely identify the defensin peptide family and subfamily, these approaches are often time-consuming and cost-ineffective. On the other hand, machine learning (ML) methods are able to effectively employ protein sequence information without the knowledge of a protein's three-dimensional structure, thus highlighting their predictive ability for the large-scale identification. To date, several ML methods have been developed for the
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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.