RT Journal Article
T1 Optimized Python library for reconstruction of ensemble-based gene co-expression networks using multi-GPU
A1 Domingo Rodríguez Baena, 
A1 López Fernández, Aurelio
A1 Gómez-Vela, Francisco Antonio
A1 Del Saz Navarro, Dulcenombre de María
A1 Delgado Cháves, Fernando M.
A1 Rodríguez Baena, Domingo Savio
K1 High-performance computing
K1 GPU
K1 Bioinformatics
K1 Data mining
K1 Big data
K1 Gene co-expression networks
AB Gene co-expression networks are valuable tools for discovering biologically relevant information within gene expression data. However, analysing large datasets presents challenges due to the identification of nonlinear gene–gene associations and the need to process an ever-growing number of gene pairs and their potential network connections. These challenges mean that some experiments are discarded because the techniques do not support these intense workloads. This paper presents pyEnGNet, a Python library that can generate gene co-expression networks in High-performance computing environments. To do this, pyEnGNet harnesses CPU and multi-GPU parallel computing resources, efficiently handling large datasets. These implementations have optimised memory management and processing, delivering timely results. We have used synthetic datasets to prove the runtime and intensive workload improvements. In addition, pyEnGNet was used in a real-life study of patients after allogeneic stem cell transplantation with invasive aspergillosis and was able to detect biological perspectives in the study.1 Introduction
PB The Journal of Supercomputing, Springer
YR 2024
FD 2024-05-11
LK https://hdl.handle.net/10433/20688
UL https://hdl.handle.net/10433/20688
LA en
NO López-Fernández, A., Gómez-Vela, F.A., del Saz-Navarro, M. et al. Optimized Python library for reconstruction of ensemble-based gene co-expression networks using multi-GPU. J Supercomput (2024). https://doi.org/10.1007/s11227-024-06127-4
NO Departamento de Deporte e Informática
DS RIO
RD May 8, 2026