<p>Performance evaluation of a DNA sequence coding scheme based on physicochemical properties in deep learning</p>

Daniela Sánchez Aristizábal; Juan  Camilo Arboleda; Pablo  Andrés Saldarriaga

doi:10.7490/f1000research.1120072.1

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Sánchez Aristizábal D, Camilo Arboleda J and Andrés Saldarriaga P. Performance evaluation of a DNA sequence coding scheme based on physicochemical properties in deep learning [version 1; not peer reviewed]. F1000Research 2025, 14:3 (poster) (https://doi.org/10.7490/f1000research.1120072.1)

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Performance evaluation of a DNA sequence coding scheme based on physicochemical properties in deep learning

Daniela Sánchez Aristizábal¹, Juan Camilo Arboleda, Pablo Andrés Saldarriaga

Published 02 Jan 2025 (https://doi.org/10.7490/f1000research.1120072.1)

Author Affiliations

¹ Antioquia, University of Antioquia, Medellín, Colombia

Copyright © 2025 Sánchez Aristizábal D et al. This is an open access work distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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CITE

Published 02 Jan 2025

Performance evaluation of a DNA sequence coding scheme based on physicochemical properties in deep learning
[version 1; not peer reviewed]

Daniela Sánchez Aristizábal¹, Juan Camilo Arboleda, Pablo Andrés Saldarriaga

Author Affiliations

¹ Antioquia, University of Antioquia, Medellín, Colombia

Presented at

ISCB-Latin America SoIBio CCBCOL Conference on Bioinformatics 2024

Abstract

Competing Interests

No competing interests were disclosed

Keywords

Deep Learning, Machine Learning, AI, DNA sequences, genomic

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