PerseuCPP: a machine learning strategy to predict cell-penetrating peptides and their uptake efficiency

Authors

Wagner Meira (ORCID: https://orcid.org/0000-0002-2614-2723)
Sabrina de Azevedo Silveira (ORCID: https://orcid.org/0000-0002-4723-2349)
Maria Cristina Baracat‐Pereira (ORCID: https://orcid.org/0000-0002-7376-2837)
Douglas E. V. Pires
Rayane Monique Bernardes-Loch
Igor Teixeira Brasiliano
Gustavo de Oliveira Almeida (ORCID: https://orcid.org/0000-0002-1812-9035)

Institutions

Universidade Federal de Minas Gerais (BR)
Universidade Federal de Viçosa (BR)
The University of Melbourne (AU)

Publication Details

Journal: Bioinformatics Advances
Published: 2024-12-26
DOI: https://doi.org/10.1093/bioadv/vbaf213
Primary Topic: RNA Interference and Gene Delivery
Type: article
Field-Weighted Citation Impact: 0.00

Funders

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
Conselho Nacional de Desenvolvimento Científico e Tecnológico
Fundação de Amparo à Pesquisa do Estado de Minas Gerais

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article

PerseuCPP: a machine learning strategy to predict cell-penetrating peptides and their uptake efficiency

Wagner Meira, Sabrina de Azevedo Silveira, Maria Cristina Baracat‐Pereira, Douglas E. V. Pires et al.

Dec 26, 2024

Bioinformatics Advances

RNA Interference and Gene Delivery

article

PerseuCPP: a machine learning strategy to predict cell-penetrating peptides and their uptake efficiency

Wagner Meira, Sabrina de Azevedo Silveira, Maria Cristina Baracat‐Pereira, Douglas E. V. Pires, Rayane Monique Bernardes-Loch, Igor Teixeira Brasiliano, Gustavo de Oliveira Almeida

December 26, 2024article en

Abstract

No abstract available for this paper.

Bioinformatics AdvancesVol. 5(1)

Universidade Federal de Minas Gerais (BR), Universidade Federal de Viçosa (BR), The University of Melbourne (AU)

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, Conselho Nacional de Desenvolvimento Científico e Tecnológico, Fundação de Amparo à Pesquisa do Estado de Minas Gerais

Openalex Percentile: Top 77%

RNA Interference and Gene Delivery

DOI Open Access PDF OpenAlex

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PerseuCPP: a machine learning strategy to predict cell-penetrating peptides and their uptake efficiency — Wagner Meira, Sabrina de Azevedo Silveira, et al. · Bioinformatics Advances (2024) | TGRS Research Map | TGRS