Author

Edwin Vera

National Polytechnic SchoolORCID 0000-0002-3335-3537Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Research Focus:benchmarking· 1 Organism:fungi (other)· 1 Assay:long-read sequencing· 1 Approach:machine learning (classical)· 1 Approach:statistical model· 1

Frequent Co-Authors

Daniel AlbujaiD· 1 shared J. Riczury Olmos-TovariD· 1 shared Paolo Maldonado· 1 shared Peter Zambrano· 1 shared

Activity by Year

2026-2026 · peak 1/yr

Co-Author Collaboration Network

4 top collaborators · 6 mutual co-author links

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1 shared paperBioinformatics

Daniel AlbujaORCID

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1 paper

Recent·Most cited

Machine Learning-Enhanced Nanopore ITS Analysis: Evaluating CPU-GPU Pipelines for High-Accuracy Fungal Taxonomic Resolution

Daniel Albuja, Peter Zambrano, Paolo Maldonado, J. Riczury Olmos-Tovar, Edwin Vera

This paper evaluates CPU and GPU-based workflows for high-accuracy fungal ITS metabarcoding using machine learning, showing GPU processing improves species-level accuracy while CPU workflows achieve genus-level concordance.

bioRxiv · Apr 7, 2026 · PreprintBioinformatics