DNA Genome Classification with Machine Learning and Image Descriptors

Daniel Prado Cussi, V. E. Machaca Arceda

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

Resumen

Sequence alignment is the most used method in Bioinformatics. Nevertheless, it is slow in time processing. For that reason, there are several methods not based on alignment to compare sequences. In this work, we analyzed Kameris and Castor, two alignment-free methods for DNA genome classification; we compared them against the most popular CNN networks: VGG16, VGG19, Resnet-50, and Inception. Also, we compared them with image descriptor methods like First-order Statistics(FOS), Gray-level Co-occurrence matrix (GLCM), Local Binary Pattern (LBP), and Multi-resolution Local Binary Pattern(MLBP), and classifiers like: Support Vector Machine (SVM), Random Forest (RF) and k-nearest neighbors (KNN). In this comparison, we concluded that FOS, GLCM, LBP, and MLBP, all with SVM got the best results in f1-score, followed by Castor and Kameris and finally by CNNs. Furthermore, Castor got a minor processing time. Finally, according to experiments, 5-mer (used by Kameris and Castor) and 6-mer outperformed 7-mer.

Idioma originalInglés
Título de la publicación alojadaAdvances in Information and Communication - Proceedings of the 2023 Future of Information and Communication Conference FICC
EditoresKohei Arai
EditorialSpringer Science and Business Media Deutschland GmbH
Páginas39-58
Número de páginas20
ISBN (versión impresa)9783031280726
DOI
EstadoPublicada - 2023
Publicado de forma externa
Evento8th Future of Information and Computing Conference, FICC 2023 - Virtual, Online
Duración: 2 mar. 20233 mar. 2023

Serie de la publicación

NombreLecture Notes in Networks and Systems
Volumen652 LNNS
ISSN (versión impresa)2367-3370
ISSN (versión digital)2367-3389

Conferencia

Conferencia8th Future of Information and Computing Conference, FICC 2023
CiudadVirtual, Online
Período2/03/233/03/23

Nota bibliográfica

Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

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