RT Journal Article
T1 Biclustering in bioinformatics using big data and High Performance Computing applications: challenges and perspectives, a review
T2 Biclustering in bioinformatics using big data and High Performance Computing applications: challenges and perspectives, a review
A1 López Fernández, Aurelio
A1 Gómez-Vela, Francisco Antonio
A1 Delgado Cháves, Fernando M.
A1 Rodríguez Baena, Domingo Savio
A1 González Dominguez, Jorge
K1 Big Data
K1 Biological Databases
K1 Data Analysis and Big Data
K1 Functional clustering
K1 Protein Databases
K1 Bioinformatics
AB 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.
PB Elsevier
YR 2025
FD 2025-07-08
LK https://hdl.handle.net/10433/24404
UL https://hdl.handle.net/10433/24404
LA en
NO J Supercomput 81, 1123 (2025).
NO Universidad Pablo de Olavide de Sevilla, Departamento de deporte e informática
DS RIO
RD May 22, 2026