Version 1
: Received: 24 September 2024 / Approved: 24 September 2024 / Online: 25 September 2024 (10:22:14 CEST)
How to cite:
Nanni, L.; Cuza, D.; Brahnam, S. AI-powered Biodiversity Assessment: Species Classification via DNA Barcoding and Deep Learning. Preprints2024, 2024091953. https://doi.org/10.20944/preprints202409.1953.v1
Nanni, L.; Cuza, D.; Brahnam, S. AI-powered Biodiversity Assessment: Species Classification via DNA Barcoding and Deep Learning. Preprints 2024, 2024091953. https://doi.org/10.20944/preprints202409.1953.v1
Nanni, L.; Cuza, D.; Brahnam, S. AI-powered Biodiversity Assessment: Species Classification via DNA Barcoding and Deep Learning. Preprints2024, 2024091953. https://doi.org/10.20944/preprints202409.1953.v1
APA Style
Nanni, L., Cuza, D., & Brahnam, S. (2024). AI-powered Biodiversity Assessment: Species Classification via DNA Barcoding and Deep Learning. Preprints. https://doi.org/10.20944/preprints202409.1953.v1
Chicago/Turabian Style
Nanni, L., Daniela Cuza and Sheryl Brahnam. 2024 "AI-powered Biodiversity Assessment: Species Classification via DNA Barcoding and Deep Learning" Preprints. https://doi.org/10.20944/preprints202409.1953.v1
Abstract
As sequencing technologies advance, short DNA sequence fragments increasingly serve as DNA barcodes for species identification. Rapid acquisition of DNA sequences from diverse organisms is now possible, highlighting the increasing significance of DNA sequence analysis tools in species identification. This study introduces a new approach for species classification with DNA barcodes based on an ensemble of deep neural networks (DNN). Several techniques are proposed and empirically evaluated for converting raw DNA sequence data into images fed into the DNNs. The best-performing approach is obtained by representing each pair of DNA bases with the value of a related physicochemical property. By utilizing different physicochemical properties, we can create an ensemble of networks. Our proposed ensemble obtains state-of-the-art performance on both simulated and real data sets. The code of the proposed approach is available at https://github.com/LorisNanni/AI-powered-Biodiversity-Assessment-Species-Classification-via-DNA-Barcoding-and-Deep-Learning .
Keywords
DNA barcoding; ensemble; convolutional neural networks
Subject
Computer Science and Mathematics, Mathematical and Computational Biology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
