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Ensemble-AMPPred: Robust AMP Prediction and Recognition Using the Ensemble Learning Method with a New Hybrid Feature for Differentiating AMPs

Genes(2021)10.3390/genes12020137Source: DataRank Database

Ensemble-AMPPred: Robust AMP Prediction and Recognition Using the Ensemble Learning Method with a New Hybrid Feature for Differentiating AMPs is a research paper published in Genes (2021). On theSindex it has a DataRank of 2.0. It has been cited 68 times, with 51 citing works in its 1-hop citation network.

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2.0DataRank · unranked

Scored on demand from live citation data

68 citations · 51 citing works in its network

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doi

DataRank reads this dataset's downstream impact straight off the citation graph — no black box, no proprietary weighting. How is this computed?

›Methodology & internals

datarank_citation_only_1hop_v6· scope data_onlyMethodology

Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring

Enrichment:Funding (1 grants)Impact (FWCI 5.96)OA: goldTopics (27)Linked Datasets (12)DataCite Reuse (12)IDs (PubMed, PMC, OpenAlex)Annotations (1)

FAIR Checklist

Context only (not used in score)

Findable (2/2)

Has DOI
Indexed in repositories

Accessible (0/2)

Interoperable (2/2)

DataCite relations
Linked datasets

Reusable (0/3)

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DataRank Breakdown

Base Score 32%Citation Network 68%

Base Score Contribution

0.635

From this paper's citation signal

Citation Network Contribution

1.4

From 42 citing papers with measurable signal

Learn more about DataRank methodology →

Authors (4)

Tayvich Vorapreeda,Apiradee Hongsthong,Chinae Thammarongtham ORCID,Supatcha Lertampaiporn ORCID

Live enrichment

PubMed:33494403 PMC:PMC7911732 OpenAlex:https://openalex.org/W3124390867

National Center for Genetic Engineering and Biotechnology (BIOTEC), Thailand

Grant: P18-51620

FWCI

5.96

Citation Percentile

1.0%

Citation Trend

2021

2022

2023

2024

2025

2026

goldLicense: cc-by

journalhttps://www.mdpi.com/2073-4425/12/2/137/pdf?version=1611305420

publisherhttps://www.mdpi.com/2073-4425/12/2/137/pdf?version=1611305420

publisherhttps://www.mdpi.com/2073-4425/12/2/137/pdf

journalhttps://doi.org/10.3390/genes12020137

repositoryhttps://pubmed.ncbi.nlm.nih.gov/33494403

Fields of Study

Antimicrobial Peptides and ActivitiesMachine Learning in BioinformaticsBiochemical and Structural Characterization

MeSH Terms

Machine LearningAlgorithmsSensitivity and SpecificitySoftwareReproducibility of ResultsAntimicrobial Cationic PeptidesDatabases, Genetic

Keywords

Ensemble learningEnsemble forecastingComputer scienceMachine learningArtificial intelligenceFalse positive paradoxFeature (linguistics)Antimicrobial peptidesSupport vector machineIdentification (biology)Sensitivity (control systems)Pattern recognition (psychology)EngineeringLogistic regressionAmp PredictionMaxprobvoteHeterogeneous Ensemble Machine Learning

Additional file 1 of BaPreS: a software tool for predicting bacteriocins using an optimal set of features

10.6084/m9.figshare.23983392.v1figshare

Additional file 1 of BaPreS: a software tool for predicting bacteriocins using an optimal set of features

10.6084/m9.figshare.23983392figshare

Additional file 2 of BaPreS: a software tool for predicting bacteriocins using an optimal set of features

10.6084/m9.figshare.23983395figshare

Additional file 2 of BaPreS: a software tool for predicting bacteriocins using an optimal set of features

10.6084/m9.figshare.23983395.v1figshare

Additional file 3 of BaPreS: a software tool for predicting bacteriocins using an optimal set of features

10.6084/m9.figshare.23983398.v1figshare

Additional file 3 of BaPreS: a software tool for predicting bacteriocins using an optimal set of features

10.6084/m9.figshare.23983398figshare

Additional file 4 of BaPreS: a software tool for predicting bacteriocins using an optimal set of features

10.6084/m9.figshare.23983401.v1figshare

Additional file 4 of BaPreS: a software tool for predicting bacteriocins using an optimal set of features

10.6084/m9.figshare.23983401figshare

Additional file 5 of BaPreS: a software tool for predicting bacteriocins using an optimal set of features

10.6084/m9.figshare.23983404.v1figshare

Additional file 5 of BaPreS: a software tool for predicting bacteriocins using an optimal set of features

10.6084/m9.figshare.23983404figshare

Additional file 6 of BaPreS: a software tool for predicting bacteriocins using an optimal set of features

10.6084/m9.figshare.23983407.v1figshare

Additional file 6 of BaPreS: a software tool for predicting bacteriocins using an optimal set of features

10.6084/m9.figshare.23983407figshare

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Ensemble-AMPPred: Robust AMP Prediction and Recognition Using the Ensemble Learning Method with a New Hybrid Feature for Differentiating AMPs

FAIR Checklist

DataRank Breakdown

Authors (4)

Funding (1 grants)

Impact

Open Access

Topics

Linked Datasets (12)

Annotations (Europe PMC)

Related Papers (10)