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}

}

@article{Lopez-Fernandez2020b,

title = {gMSR: A Multi-GPU Algorithm to Accelerate a Massive Validation of Biclusters},

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

url = {https://www.mdpi.com/2079-9292/9/11/1782},

doi = {10.3390/electronics9111782},

issn = {2079-9292},

year  = {2020},

date = {2020-10-27},

journal = {Electronics},

volume = {9},

number = {11},

pages = {1782},

abstract = {Nowadays, Biclustering is one of the most widely used machine learning techniques to discover local patterns in datasets from different areas such as energy consumption, marketing, social networks or bioinformatics, among them. Particularly in bioinformatics, Biclustering techniques have become extremely time-consuming, also being huge the number of results generated, due to the continuous increase in the size of the databases over the last few years. For this reason, validation techniques must be adapted to this new environment in order to help researchers focus their efforts on a specific subset of results in an efficient, fast and reliable way. The aforementioned situation may well be considered as Big Data context. In this sense, multiple machine learning techniques have been implemented by the application of Graphic Processing Units (GPU) technology and CUDA architecture to accelerate the processing of large databases. However, as far as we know, this technology has not yet been applied to any bicluster validation technique. In this work, a multi-GPU version of one of the most used bicluster validation measure, Mean Squared Residue (MSR), is presented. It takes advantage of all the hardware and memory resources offered by GPU devices. Because of to this, gMSR is able to validate a massive number of biclusters in any Biclustering-based study within a Big Data context.},

keywords = {Biclustering, Biclustering validation, CUDA, GPU, MSR},

pubstate = {published},

tppubtype = {article}

}