Classification of Organisms into Kingdoms using DNA Codon Frequency

• Autor: Palma Ttito, Luis Beltrán
• Formato: artículo
• Fecha de Publicación: 2022
• Institución: Universidad de Lima
• Repositorio: Revistas - Universidad de Lima
• Lenguaje: español
• OAI Identifier: oai:ojs.pkp.sfu.ca:article/5896
• Enlace del recurso: https://revistas.ulima.edu.pe/index.php/Interfases/article/view/5896
• Nivel de acceso: acceso abierto
• Materia: machine learning; Ensembles; DNA codon frequency; kingdom; ensambles; frecuencia de codones ADN; reino

This study aims to use machine learning classifiers to predict the kingdom to which an organism belongs by the frequency of use of DNA codons. The study used 13,028 data from GenBank organisms distributed in eleven kingdoms and reduced them to six kingdoms (archaea, bacteria, invertebrates, plants, viruses, and vertebrates) with 9,027 regrouped data. The process required cleaning irrelevant attributes, using measurement metrics of accuracy, precision, sensitivity, and score classifiers, and the adjustment of hyperparameters of the models. The classification algorithms were voting, bagging, boosting, and stacking, using KNN, AD, MLP, SVC, and RF. Random forest was used in selecting the attributes. The stacking ensemble, with its models, better predicts the classification of organisms in the present study.