Publications

@article{Lopez-Fernandez2024b,

title = {Optimized Python library for reconstruction of ensemble-based gene co-expression networks using multi-GPU},

author = {A. Lopez-Fernandez and F. Gómez-Vela and Dulcenombre M. Saz-Navarro and F. M. Delgado and D. Rodríguez-Baena},

url = {https://link.springer.com/article/10.1007/s11227-024-06127-4},

doi = {10.1007/s11227-024-06127-4},

issn = {1573-0484},

year  = {2024},

date = {2024-05-11},

urldate = {2024-05-11},

journal = {The Journal of Supercomputing},

abstract = {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.},

keywords = {Big Data, Bioinformatics, Data Mining, Gene co-expression network, GPU, High-Performance Computing},

pubstate = {published},

tppubtype = {article}

}

@article{Lopez-Fernandez2020,

title = {A multi-GPU biclustering algorithm for binary datasets},

author = {A. Lopez-Fernandez and D. Rodríguez-Baena and F. Gómez-Vela and F. Divina and M. García-Torres},

url = {https://www.sciencedirect.com/science/article/pii/S0743731520303701},

doi = {10.1016/j.jpdc.2020.09.009},

issn = {0743-7315},

year  = {2021},

date = {2021-01-01},

journal = {Journal of Parallel and Distributed Computing},

volume = {147},

pages = {209-219},

abstract = {Graphics Processing Units technology (GPU) and CUDA architecture are one of the most used options to adapt machine learning techniques to the huge amounts of complex data that are currently generated. Biclustering techniques are useful for discovering local patterns in datasets. Those of them that have been implemented to use GPU resources in parallel have improved their computational performance. However, this fact does not guarantee that they can successfully process large datasets. There are some important issues that must be taken into account, like the data transfers between CPU and GPU memory or the balanced distribution of workload between the GPU resources. In this paper, a GPU version of one of the fastest biclustering solutions, BiBit, is presented. This implementation, named gBiBit, has been designed to take full advantage of the computational resources offered by GPU devices. Either using a single GPU device or in its multi-GPU mode, gBiBit is able to process large binary datasets. The experimental results have shown that gBiBit improves the computational performance of BiBit, a CPU parallel version and an early GPU version, called ParBiBit and CUBiBit, respectively. gBiBit source code is available at https://github.com/aureliolfdez/gbibit.},

keywords = {Biclustering, Big Data, CUDA, GPU},

pubstate = {published},

tppubtype = {article}

}