Aurelio López Fernández

@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.},

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@article{Figueroa-Martinez2024,

title = {Computational Ensemble Gene Co-Expression Networks for the Analysis of Cancer Biomarkers},

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

url = {https://www.mdpi.com/2227-9709/11/2/14},

doi = {10.3390/informatics11020014},

issn = {2227-9709},

year  = {2024},

date = {2024-03-26},

journal = {Informatics},

volume = {11},

number = {2},

pages = {14},

abstract = {Gene networks have become a powerful tool for the comprehensive examination of gene expression patterns. Thanks to these networks generated by means of inference algorithms, it is possible to study different biological processes and even identify new biomarkers for such diseases. These biomarkers are essential for the discovery of new treatments for genetic diseases such as cancer. In this work, we introduce an algorithm for genetic network inference based on an ensemble method that improves the robustness of the results by combining two main steps: first, the evaluation of the relationship between pairs of genes using three different co-expression measures, and, subsequently, a voting strategy. The utility of this approach was demonstrated by applying it to a human dataset encompassing breast and prostate cancer-associated stromal cells. Two gene networks were computed using microarray data, one for breast cancer and one for prostate cancer. The results obtained revealed, on the one hand, distinct stromal cell behaviors in breast and prostate cancer and, on the other hand, a list of potential biomarkers for both diseases. In the case of breast tumor, ST6GAL2, RIPOR3, COL5A1, and DEPDC7 were found, and in the case of prostate tumor, the genes were GATA6-AS1, ARFGEF3, PRR15L, and APBA2. These results demonstrate the usefulness of the ensemble method in the field of biomarker discovery.},

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}

@article{Lopez-Fernandez2024,

title = {bioScience: A new python science library for high-performance computing bioinformatics analytics},

author = {A. Lopez-Fernandez and F. Gómez-Vela and J. González-Domínguez and P. Bidare-Divakarachari},

url = {https://www.sciencedirect.com/science/article/pii/S2352711024000372},

doi = {10.1016/j.softx.2024.101666},

issn = {2352-7110},

year  = {2024},

date = {2024-02-19},

journal = {SoftwareX},

volume = {26},

pages = {101666},

abstract = {BioScience is an advanced Python library designed to satisfy the growing data analysis needs in the field of bioinformatics by leveraging High-Performance Computing (HPC). This library encompasses a vast multitude of functionalities, from loading specialized gene expression datasets (microarrays, RNA-Seq, etc.) to preprocessing techniques and data mining algorithms suitable for this type of datasets. BioScience is distinguished by its capacity to manage large amounts of biological data, providing users with efficient and scalable tools for the analysis of genomic and transcriptomic data through the use of parallel architectures for clusters composed of CPUs and GPUs.},

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@article{Saz-Navarro2024,

title = {CyEnGNet—App: A new Cytoscape app for the reconstruction of large co-expression networks using an ensemble approach},

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

url = {https://www.sciencedirect.com/science/article/pii/S2352711024000050},

doi = {10.1016/j.softx.2024.101634},

issn = {2352-7110},

year  = {2024},

date = {2024-01-18},

urldate = {2024-01-18},

journal = {SoftwareX},

volume = {25},

pages = {101634},

abstract = {The construction of gene co-expression networks is an essential tool in Bioinformatics for discovering useful biological knowledge. There are a multitude of methodologies related to the construction of this type of network, and one of them is EnGNet, which carries out a joint and greedy approach to the reconstruction of large gene coexpression networks. This work introduces CyEnGNet-App, a Cytoscape application designed to integrate and leverage the EnGNet algorithm. The application allows dynamic interaction and visualisation of gene networks and integration with other Cytoscape applications. CyEnGNet-App is a valuable addition to the field of Bioinformatics, improving the reconstruction of genetic networks and providing a more accessible and efficient user experience in Cytoscape.},

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@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.},

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pubstate = {published},

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}

@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.},

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@article{Aram2020,

title = {The integration of heterogeneous information from diverse disciplines regarding persons and goods},

author = {B. Aram and A. Lopez-Fernandez and D. Muñiz-Amian},

doi = {10.1093/llc/fqaa021},

year  = {2020},

date = {2020-06-24},

journal = {Digital Scholarship in the Humanities},

abstract = {This article presents a relational database capable of integrating data from a variety of types of written sources as well as material remains. In response to historical research questions, information from such diverse sources as documentary, bioanthropological, isotopic, and DNA analyses has been assessed, homogenized, and situated in time and space. Multidisciplinary ontologies offer complementary and integrated perspectives regarding persons and goods. While responding to specific research questions about the impact of globalization on the isthmus of Panama during the sixteenth and seventeenth centuries, the data model and user interface promote the ongoing interrogation of diverse information about complex, changing societies. To this end, the application designed makes it possible to search, consult, and download data that researchers have contributed from anywhere in the world.},

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pubstate = {published},

tppubtype = {article}

}

@article{Lopez-Fernandez2018,

title = {BIGO: A web application to analyse gene enrichment analysis results},

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

url = {http://www.sciencedirect.com/science/article/pii/S147692711730823X},

doi = {10.1016/j.compbiolchem.2018.06.006},

issn = {1476-9271},

year  = {2018},

date = {2018-05-01},

journal = {Computational biology and chemistry},

volume = {76},

pages = {169-178},

abstract = {Background and objective

Gene enrichment tools enable the analysis of the relationships between genes with biological annotations stored in biological databases. The results obtained by these tools are usually difficult to analyse. Therefore, researchers require new tools with friendly user interfaces available on all types of devices and new methods to make the analysis of the results easier.



Methods

In this work, we present the BIGO Web tool. BIGO is a friendly Web tool to perform enrichment analyses of a collection of gene sets. On the basis of the obtained enrichment analysis results, BIGO combines the biological terms to organize them and graphically represents the relationships between gene sets to make the interpretations of the results easier.



Results

BIGO offers useful services that provide the opportunity to focus on a concrete subset of results by discarding too general biological terms or to obtain useful knowledge by means of the visual analysis of the functional connections between the sets of genes being analysed.



Conclusions

BIGO is a web tool with a novel and modern design that provides the possibility to improve the analysis tasks applied to gene enrichment results.},

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