Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Articles Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Novel HGDBO: A Hybrid Genetic and Dung Beetle Optimization Algorithm for Microarray Gene Selection and Efficient Cancer Classification; [Nuevo HGDBO: Un Algoritmo Hrido de Optimizaci Genica y de Escarabajos Peloteros para la Selecci de Genes en Microrrays y la Clasificaci Eficiente del Ccer] Subject The topic of the resource Dung Beetle Optimization; Evolutionary Computation; Gene Feature Selection; Genetic Algorithm; Hybrid Algorithm; Microarray Data Description An account of the resource Introduction: ovarian cancer ranked as the seventh most common cancer and the eighth leading cause of cancer-related mortality among women globally. Early detection was crucial for improving survival rates, emphasizing the need for better screening techniques and increased awareness. Microarray gene data, containing numerous genes across multiple samples, presented both opportunities and challenges in understanding gene functions and disease pathways. This research focused on reducing feature selection time in large gene expression datasets by applying a hybrid bio-inspired method, HGDBO. The goal was to enhance classification accuracy by optimizing gene subsets for improved gene expression analysis. Method: the study introduced a novel hybrid feature selection method called HGDBO, which combined the Dung Beetle Optimization (DBO) algorithm with the Genetic Algorithm (GA) to improve microarray data analysis. The HGDBO method leveraged the exploratory strengths of DBO and the exploitative capabilities of GA to identify relevant genes for disease classification. Experiments conducted on multiple microarray datasets showed that the hybrid approach offered superior classification performance, stability, and computational efficiency compared to traditional methods. Ovarian cancer classification was performed using Nae Bayes (NB) and Random Forest (RF) algorithms. Results and Discussion: the Random Forest model outperformed the Nae Bayes model across all metrics, achieving higher accuracy (0,96 vs. 0,91), precision (0,95 vs. 0,91), recall (0,97 vs. 0,90), F1 score (0,95 vs. 0,91), and specificity (0,97 vs. 0,86). Conclusions: these results demonstrated the effectiveness of the HGDBO method and the Random Forest classifier in improving the analysis and classification of ovarian cancer using microarray gene data. 2024; Los autores. Creator An entity primarily responsible for making the resource Alluri V.L.; Kanadam K.P.; Helen Josephine V.L. Source A related resource from which the described resource is derived Data and Metadata, Vol-3 Publisher An entity responsible for making the resource available Editorial Salud, Ciencia y Tecnologia Date A point or period of time associated with an event in the lifecycle of the resource 2024-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.56294/dm2024.420" target="_blank" rel="noreferrer noopener">https://doi.org/10.56294/dm2024.420</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85207482749&amp;doi=10.56294%2Fdm2024.420&amp;partnerID=40&amp;md5=810dd1eb2adb0091f576c48c22df319b" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85207482749&amp;doi=10.56294%2fdm2024.420&amp;partnerID=40&amp;md5=810dd1eb2adb0091f576c48c22df319b</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 29534917 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Article Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Alluri V.L., Acharya Nagarjuna University, Department of Computer Science and Engineering, AP, Guntur, India; Kanadam K.P., R.V.R &amp; J.C. College of Engineering, Department of Computer Applications, AP, Guntur, India; Helen Josephine V.L., Christ University, Business Analytics, School of Business and Management, Karnataka, Bangalore, India