Biclustering in bioinformatics using big data and High Performance Computing applications: challenges and perspectives, a review

Biclustering is a powerful machine learning technique that simultaneously groups rows and columns in matrix-based datasets. Applied to gene expression data in bioinformatics, its use has expanded alongside the rapid growth of high-throughput sequencing technologies, leading to massive and complex bi...

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Autores: López Fernández, Aurelio, Gómez-Vela, Francisco Antonio, Delgado Cháves, Fernando M., Rodríguez Baena, Domingo Savio, González Dominguez, Jorge
Tipo de recurso: artículo
Fecha de publicación:2025
País:España
Institución:Universidad Pablo de Olavide (UPO)
Repositorio:RIO. Repositorio Institucional Olavide
Idioma:inglés
OAI Identifier:oai:rio.upo.es:10433/24404
Acceso en línea:https://hdl.handle.net/10433/24404
Access Level:acceso abierto
Palabra clave:Big Data
Biological Databases
Data Analysis and Big Data
Functional clustering
Protein Databases
Bioinformatics
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spelling Biclustering in bioinformatics using big data and High Performance Computing applications: challenges and perspectives, a reviewBiclustering in bioinformatics using big data and High Performance Computing applications: challenges and perspectives, a reviewLópez Fernández, AurelioGómez-Vela, Francisco AntonioDelgado Cháves, Fernando M.Rodríguez Baena, Domingo SavioGonzález Dominguez, JorgeBig DataBiological DatabasesData Analysis and Big DataFunctional clusteringProtein DatabasesBioinformaticsBiclustering is a powerful machine learning technique that simultaneously groups rows and columns in matrix-based datasets. Applied to gene expression data in bioinformatics, its use has expanded alongside the rapid growth of high-throughput sequencing technologies, leading to massive and complex biological datasets. This review aims to examine how biclustering methods and their validation strategies are evolving to meet the demands of High Performance Computing (HPC) and Big Data environments. We present a structured classification of existing approaches based on the computational paradigms they employ, including MPI/OpenMP, Apache Hadoop/Spark, and GPU/CUDA. By synthesising these developments, we highlight current trends and outline key research challenges. The knowledge gathered in this work may support researchers in adapting and scaling biclustering algorithms to analyse large-scale biomedical data more efficiently. Our contribution is intended to bridge the gap between algorithmic innovation and computational scalability in the context of bioinformatics and data-intensive applications.Elsevier20252025-07-1020252025-07-0820252025-07-08journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/10433/24404reponame:RIO. Repositorio Institucional Olavideinstname:Universidad Pablo de Olavide (UPO)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Attribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:rio.upo.es:10433/244042026-06-13T12:46:27Z
dc.title.none.fl_str_mv Biclustering in bioinformatics using big data and High Performance Computing applications: challenges and perspectives, a review
Biclustering in bioinformatics using big data and High Performance Computing applications: challenges and perspectives, a review
title Biclustering in bioinformatics using big data and High Performance Computing applications: challenges and perspectives, a review
spellingShingle Biclustering in bioinformatics using big data and High Performance Computing applications: challenges and perspectives, a review
López Fernández, Aurelio
Big Data
Biological Databases
Data Analysis and Big Data
Functional clustering
Protein Databases
Bioinformatics
title_short Biclustering in bioinformatics using big data and High Performance Computing applications: challenges and perspectives, a review
title_full Biclustering in bioinformatics using big data and High Performance Computing applications: challenges and perspectives, a review
title_fullStr Biclustering in bioinformatics using big data and High Performance Computing applications: challenges and perspectives, a review
title_full_unstemmed Biclustering in bioinformatics using big data and High Performance Computing applications: challenges and perspectives, a review
title_sort Biclustering in bioinformatics using big data and High Performance Computing applications: challenges and perspectives, a review
dc.creator.none.fl_str_mv López Fernández, Aurelio
Gómez-Vela, Francisco Antonio
Delgado Cháves, Fernando M.
Rodríguez Baena, Domingo Savio
González Dominguez, Jorge
author López Fernández, Aurelio
author_facet López Fernández, Aurelio
Gómez-Vela, Francisco Antonio
Delgado Cháves, Fernando M.
Rodríguez Baena, Domingo Savio
González Dominguez, Jorge
author_role author
author2 Gómez-Vela, Francisco Antonio
Delgado Cháves, Fernando M.
Rodríguez Baena, Domingo Savio
González Dominguez, Jorge
author2_role author
author
author
author
dc.contributor.none.fl_str_mv
dc.subject.none.fl_str_mv Big Data
Biological Databases
Data Analysis and Big Data
Functional clustering
Protein Databases
Bioinformatics
topic Big Data
Biological Databases
Data Analysis and Big Data
Functional clustering
Protein Databases
Bioinformatics
description Biclustering is a powerful machine learning technique that simultaneously groups rows and columns in matrix-based datasets. Applied to gene expression data in bioinformatics, its use has expanded alongside the rapid growth of high-throughput sequencing technologies, leading to massive and complex biological datasets. This review aims to examine how biclustering methods and their validation strategies are evolving to meet the demands of High Performance Computing (HPC) and Big Data environments. We present a structured classification of existing approaches based on the computational paradigms they employ, including MPI/OpenMP, Apache Hadoop/Spark, and GPU/CUDA. By synthesising these developments, we highlight current trends and outline key research challenges. The knowledge gathered in this work may support researchers in adapting and scaling biclustering algorithms to analyse large-scale biomedical data more efficiently. Our contribution is intended to bridge the gap between algorithmic innovation and computational scalability in the context of bioinformatics and data-intensive applications.
publishDate 2025
dc.date.none.fl_str_mv 2025
2025-07-10
2025
2025-07-08
2025
2025-07-08
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/10433/24404
url https://hdl.handle.net/10433/24404
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:RIO. Repositorio Institucional Olavide
instname:Universidad Pablo de Olavide (UPO)
instname_str Universidad Pablo de Olavide (UPO)
reponame_str RIO. Repositorio Institucional Olavide
collection RIO. Repositorio Institucional Olavide
repository.name.fl_str_mv
repository.mail.fl_str_mv
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