2026-06-05T14:41:13Zhttps://riuma.uma.es/rest/oai/request

oai:riuma.uma.es:10630/388982026-02-03T10:56:33Zcom_10630_2254col_10630_37953

Exhaustive biclustering driven by self-learning evolutionary approach for biomedical data Segura Ortiz, Adrián José-García, Adán Jourdan, Laetitia García-Nieto, José Manuel Computación evolutiva Biomedicina - Investigación Biomedicina - Innovaciones tecnológicas Background and Objective: Biclustering is a key data analysis technique that identifies submatrices with coherent patterns, widely applied in biomedical fields such as gene co-expression analysis. Despite its importance, in the context of evolutionary algorithms, traditional partial representations in biclustering algorithms face significant limitations, such as redundancy and limited adaptability to domain-specific objectives. This study aims to overcome these challenges by introducing MOEBA-BIO, a new evolutionary biclustering framework for biomedical data. Methods: MOEBA-BIO is designed as a flexible framework based on the evolutionary metaheuristics scheme. It includes a self-configurator that dynamically adjusts the algorithm’s objectives and parameters based on contextual domain knowledge. The framework employs a complete representation, enabling the integration of new domain-specific objectives and the self-determination of the number of biclusters, addressing the limitations of traditional representations. The source code is available through the following git repository: https://github.com/AdrianSeguraOrtiz/MOEBA-BIO. Results: Experimental results demonstrate that MOEBA-BIO overcomes the limitations of classical partial representations. Furthermore, its application to simulated and real-world gene expression datasets highlights its ability to specialize in specific biological domains, improving accuracy and functional enrichment of biclusters compared to other state-of-the-art techniques. Conclusions: MOEBA-BIO represents a significant advancement in biclustering applied to bioinformatics. Its innovative framework, combining adaptability, self-configuration, and integration of domain-specific objectives, addresses the main limitations of traditional methods and offers robust solutions for complex biomedical datasets. 2025-06-05T11:38:00Z 2025-06-05T11:38:00Z 2025-06-05T11:38:00Z 2025-05-29 journal article Segura-Ortiz, A., José-García, A., Jourdan, L., & García-Nieto, J. (2025). Exhaustive biclustering driven by self-learning evolutionary approach for biomedical data. Computer Methods and Programs in Biomedicine, 269, 108846. https://doi.org/10.1016/J.CMPB.2025.108846 0169-2607 https://hdl.handle.net/10630/38898 10.1016/j.cmpb.2025.108846 eng http://creativecommons.org/licenses/by/4.0/ open access Atribución 4.0 Internacional Elsevier