Author

Gustavo de Oliveira Almeida

Universidade Federal de ViçosaORCID 0000-0001-8891-2431Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 2

Signature Assays, Organisms & Methods

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Approach:machine learning (classical)· 2 Research Focus:benchmarking· 1 Approach:deep learning· 1 Research Focus:differential expression· 1 Research Focus:drug response / drug discovery· 1 Approach:graph neural network· 1 Research Focus:protein design· 1 Assay:protein sequence· 1

Frequent Co-Authors

Sabrina de Azevedo SilveiraiD· 2 shared Wagner MeiraiD· 2 shared Bruno Marques SilvaiD· 1 shared Douglas E. V. PiresiD· 1 shared Douglas V. Pires· 1 shared Igor Teixeira Brasiliano· 1 shared Maria Cristina Baracat‐PereiraiD· 1 shared Matheus C. AlbuquerqueiD· 1 shared

Activity by Year

2024-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 29 mutual co-author links

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2 shared papersBioinformatics

Sabrina de Azevedo SilveiraORCID

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2 papers

Recent·Most cited

BioinformaticsbioRxivPreprintOct 15, 2026

NoiTox: A Machine Learning Strategy for Peptide Toxicity Prediction Reveals Label Noise in Benchmark Datasets

Gustavo de Oliveira Almeida, Matheus C. Albuquerque, Bruno Marques Silva…·Sabrina de Azevedo Silveira

NoiTox presents a multi-model approach for peptide toxicity prediction, revealing label noise in datasets and showing that physicochemical descriptors are informative.

DOI 10.64898/2026.10.08.757741

proteomicsmachine learning (classical)

𝕏 PostBluesky

BioinformaticsBioinformatics AdvancesPeer-reviewedDec 26, 2024

PERSEUcpp: A machine learning strategy to predictcell-penetrating peptides and their uptake efficiency

Rayane Monique Bernardes Loch, Gustavo de Oliveira Almeida, Igor Teixeira Brasiliano…·Sabrina de Azevedo Silveira

PERSEUcpp predicts cell-penetrating peptides and their uptake efficiency using machine learning, achieving high accuracy and interpretability.

5 citations

protein sequencemachine learning (classical)

𝕏 PostBluesky