Exhaustive biclustering driven by self-learning evolutionary approach for biomedical data
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.
Authors
- Adán José-García (ORCID: https://orcid.org/0000-0003-2623-5206)
- José García-Nieto (ORCID: https://orcid.org/0000-0003-2985-3480)
- Adrián Segura-Ortiz (ORCID: https://orcid.org/0000-0003-2149-5754)
- Laëtitia Jourdan (ORCID: https://orcid.org/0000-0002-4170-6830)
Institutions
- Centre National de la Recherche Scientifique (FR)
- Inserm (FR)
- Université de Lille (FR)
- Centre Hospitalier Universitaire de Lille (FR)
- Lille Inflammation Research International Center (FR)
- École Centrale de Lille (FR)
- Universidad de Málaga (ES)
Publication Details
- Journal
- Computer Methods and Programs in Biomedicine
- Published
- 2025-05-29
- DOI
- https://doi.org/10.1016/j.cmpb.2025.108846
- Citations
- 1
- Primary Topic
- Evolutionary Algorithms and Applications
- Type
- article
- Field-Weighted Citation Impact
- 2.48