ArticlePeerJ2019
Toward insights on determining factors for high activity in antimicrobial peptides via machine learning.
Article in PeerJ, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
7 citing papers in PubMed, 14 citations in OpenAlex.
- Olive Leaf Protein Hydrolysate as a Novel Source of Antimicrobial Peptides: Peptidomic Characterization and In Silico Evaluation.Molecules (Basel, Switzerland) · 2025Article
- Comparative transcriptome analysis reveals various host responses to sacbrood virus infection in Apis cerana and Apis mellifera.BMC genomics · 2025Article
- An active machine learning discovery platform for membrane-disrupting and pore-forming peptides.Physical chemistry chemical physics : PCCP · 2024Article
- Comparative analysis of machine learning algorithms on the microbial strain-specific AMP prediction.Briefings in bioinformatics · 2022Article
- Physicochemical Features and Peculiarities of Interaction of AMP with the Membrane.Pharmaceuticals (Basel, Switzerland) · 2021Review
- Beyond Tripeptides Two-Step Active Machine Learning for Very Large Data sets.Journal of chemical theory and computation · 2021Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The continued and general rise of antibiotic resistance in pathogenic microbes is a well-recognized global threat. Host defense peptides (HDPs), a component of the innate immune system have demonstrated promising potential to become a next generation antibiotic effective against a plethora of pathogens. While the effectiveness of antimicrobial HDPs has been extensively demonstrated in experimental studies, theoretical insights on the mechanism by which these peptides function is comparably limited. In particular, experimental studies of AMP mechanisms are limited in the number of different peptides investigated and the type of peptide parameters considered. This study makes use of the random forest algorithm for classifying the antimicrobial activity as well for identifying molecular descriptors underpinning the antimicrobial activity of investigated peptides. Subsequent manual interpretation of the identified important descriptors revealed that polarity-solubility are necessary for the membrane lytic antimicrobial activity of HDPs.
Indexed as
Identifiers
What OpenQuestion holds
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.