Aurelio López-Fernández obtained his PhD in Computer Science from the Pablo de Olavide University of Seville, obtaining a qualification of Cum Laude. In addition to BS Degree and the MS Degree in Computer Science from the Pablo de Olavide University.
His researchs lines are based on the application of new algorithms for the exploration and extraction of knowledge from massive data sources using applications related to Big Data and high-performance computing (HPC).
Teaching
Computer Science (Information Systems), Pablo de Olavide University.
- Fundamentals of Programming.
- Fundamentals of Information Systems.
- Management Information Systems Technologies.
- Final Degree Project (TFG).
Related links
Publications
2025 |
A. Lopez-Fernandez; F. Gómez-Vela; D. Rodríguez-Baena; F. Gómez-Vela; J. González-Domínguez Biclustering in bioinformatics using big data and High Performance Computing applications: challenges and perspectives, a review Journal Article In: The Journal of Supercomputing, vol. 81, pp. 1123, 2025. @article{Lopez-Fernandez2025b,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. |
M. Ríos-Cadenas; A. Lopez-Fernandez; F. Gómez-Vela; J. A. Ortega; I. Rincón Perez 2025 IEEE 38th International Symposium on Computer-Based Medical Systems (CBMS), 2025. @conference{nokey,Soft tissue sarcomas (STS), such as leiomyosarcoma (LMS) and malignant peripheral nerve sheath tumors (MPNST), are aggressive neoplasms with limited treatment alternatives. Comprehending their biological mechanisms requires the examination of gene expression data, provides insights into transcriptional activity. Gene co-expression networks (GCNs) are essential for elucidating functional gene linkages within datasets, depicting genes as nodes and their interactions as edges. Such methods are typically employed for the discovery of potential biomarkers. However, the computational performance for analysing huge genomic datasets requires High-Performance Computing (HPC) technologies, such as GPGPU and distributed computing models, to improve scalability and efficiency. This study uses HPC-based GCN techniques to uncover potential biomarkers associated with the aggressiveness of LMS and MPNST. Through the comparison of co-expression networks from malignant tumor tissues and their normal or benign counterparts, we identify genes exhibiting differential expression patterns that may facilitate sarcoma growth. As a result, six potential biomarkers were identified in the study. This holistic approach improves our comprehension of STS biology while providing novel tools for biomarker identification and prospective therapeutic targeting. |
D. Rodríguez-Baena; F. Gómez-Vela; A. Lopez-Fernandez; M. García-Torres; F. Divina BinRec: addressing data sparsity and cold-start challenges in recommender systems with biclustering Journal Article In: Applied Intelligence, vol. 55, no. 12, pp. 830, 2025. @article{Rodríguez-Baena2025,Recommender Systems help users in making decision in different fields such as purchases or what movies to watch. User-Based Collaborative Filtering (UBCF) approach is one of the most commonly used techniques for developing these software tools. It is based on the idea that users who have previously shared similar tastes will almost certainly share similar tastes in the future. As a result, determining the nearest users to the one for whom recommendations are sought (active user) is critical. However, the massive growth of online commercial data has made this task especially difficult. As a result, Biclustering techniques have been used in recent years to perform a local search for the nearest users in subgroups of users with similar rating behaviour under a subgroup of items (biclusters), rather than searching the entire rating database. Nevertheless, due to the large size of these databases, the number of biclusters generated can be extremely high, making their processing very complex. In this paper we propose BinRec, a novel UBCF approach based on Biclustering. BinRec simplifies the search for neighbouring users by determining which ones are nearest to the active user based on the number of biclusters shared by the users. Experimental results show that BinRec outperforms other state-of-the-art recommender systems, with a remarkable improvement in environments with high data sparsity. The flexibility and scalability of the method position it as an efficient alternative for common collaborative filtering problems such as sparsity or cold-start. |
A. Lopez-Fernandez; F. Divina; F. Gómez-Vela; M. García-Torres Data mining for enhancing learning and assessment to a microcompetence-based methodology in higher education Journal Article In: IEEE Revista Iberoamericana de Tecnologias del Aprendizaje, vol. 20, pp. 22-31, 2025. @article{Lopez-Fernandez2025,This work introduces an innovative teaching methodology based on microcompetences applied in a higher education context. The intervention involved creating a repository of practical case studies in the form of quizzes and integrating microcompetences into each course activity. The digital tool Sapiens was used to identify learning deficiencies and provide both collective and individualized feedback. The results indicate a significant increase in student participation and academic performance compared to previous years. Furthermore, students voluntarily used virtual teaching modalities to reinforce their knowledge, particularly in more complex areas. Data mining techniques identified performance patterns among students, highlighting the methodology’s effectiveness in improving both transversal and specific competences. The study’s findings underscore the importance of implementing microcompetency-based methodologies in higher education to enhance the quality of learning and continuous assessment. This approach not only facilitated a deeper understanding of course content but also promoted critical thinking, abstract reasoning, and interpersonal skills, preparing students for future academic and professional challenges. Additionally, the flexibility and adaptability of the digital tools used provided a seamless transition across different teaching modalities, such as in-person, hybrid, and online formats. Thus, the implementation of this innovative methodology has demonstrated its potential to significantly improve student engagement, participation, and academic success, thereby contributing to a more effective and comprehensive educational experience in higher education. |
2024 |
D.R. Insfrán-Coronel; E.M. Enrique-Sánchez; Federico Beck; A. Lopez-Fernandez; M. García-Torres Analysis of School Dropout Rate in Paraguay Using a Machine Learning Approach Conference International Joint Conferences: 15th International Conference on European Transnational Education (ICEUTE 2024), Springer Nature Switzerland, 2024, ISBN: 978-3-031-75016-8. @conference{Insfrán-Coronel2024,This study investigates the school dropout rates in Paraguay, focusing on the transition from ninth grade to the first year of secondary school in the Concepción department. Using an extract, transform, and load (ETL) process, data from the Paraguayan Ministry of Education and Science and the National Institute of Statistics were analyzed. The research employs clustering techniques, particularly K-means, to identify patterns and risk profiles among students. The findings highlight the significant impact of socio-economic factors, such as poverty and child labor, on school dropout rates. These insights aim to inform targeted interventions to improve educational outcomes and reduce dropout rates in Paraguay. |
S. Vázquez-Noguera; F. Martínez; D. Becerra-Alonso; A. Lopez-Fernandez; I. Lopez-Cobo; P. Sosa Effects of Language of Instruction in Higher Education Conference International Joint Conferences: 15th International Conference on European Transnational Education (ICEUTE 2024) , Springer Nature Switzerland, 2024, ISBN: 978-3-031-75016-8. @conference{Vázquez-Noguera2024,In higher education, educational centers are paying attention to student mobility and internationalization to increase visibility. This increase in the intake of international students has resulted in not only improved global competitiveness, but also economic benefits. However, in order to develop education in a language different from the mother tongue, it requires high-level proficiency in such a language. Therefore, in this work, we explore the impact of the language of instruction on business administration and management grade from Universidad Loyola (Spain). We analyze and compare the student profile according to the language of instruction using a clustering approach. The results suggest that students who receive instruction in a foreign language achieve better performance than those who receive it in their mother tongue. However, the number of students who decide to study in a foreign language is smaller. |
J.A. Torres-Báez; J.B. Torres-Báez; A. Lopez-Fernandez; F. Gomez-Vela; Federico J Beck Exploring Educational Trends: Specializations in Secondary Education in Paraguay from 2018 to 2021 Conference International Joint Conferences: 15th International Conference on European Transnational Education (ICEUTE 2024) , Springer Nature Switzerland, 2024, ISBN: 978-3-031-75016-8. @conference{Torres-Báez2024,Paraguay’s education system has undergone significant changes, particularly at the secondary level, introducing new specializations and teaching methods. While this diversity offers students unique opportunities, it also presents challenges in selecting a suitable specialty. Examining the variety and demand of specializations provides insights into educational trends and job market needs. Analyzing gender distribution across fields can help address disparities. Additionally, factors like accessibility, curriculum variety, overage students, and indigenous inclusion must be considered. Advanced methods like exploratory data analysis (EDA) are essential for understanding these complexities. This study introduces a tool for EDA and comprehensive investigation of enrollment data, aiming to provide valuable insights for students. The importance of EDA in educational research is emphasized, along with advancements … |
A. Lopez-Fernandez; J. Gallejones-Eskubi; Dulcenombre M. Saz-Navarro; F. Gómez-Vela Breast Cancer Biomarker Analysis Using Gene Co-expression Networks Conference IWBBIO 2024: International Work-Conference on Bioinformatics and Biomedical Engineering , Springer Nature Switzerland, 2024, ISBN: 978-3-031-64636-2. @conference{Lopez-Fernandez2024c,Gene co-expression networks have emerged as a robust tool for conducting comprehensive analyses of gene expression patterns. These networks, constructed through inference algorithms, facilitate the exploration of various biological processes and enable the identification of novel biomarkers from which to explore new lines of disease research. This work found that breast cancer stromal cells are strongly dysregulated in genes related to modifications in cellular structures that hold stromal tissue cells together, inflammatory responses, and molecules implicated in immune system regulation. Finally, ANAPC11, LRFN5, COL8A2, TEX11, DOCK9, CPLX1, LONP2, and LAT2 biomarkers were suggested in the context of stromal breast tumors. |
A. Lopez-Fernandez; F. Gómez-Vela; Dulcenombre M. Saz-Navarro; F. M. Delgado; D. Rodríguez-Baena Optimized Python library for reconstruction of ensemble-based gene co-expression networks using multi-GPU Journal Article In: The Journal of Supercomputing, 2024, ISSN: 1573-0484. @article{Lopez-Fernandez2024b,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. |
J. Figueroa-Martinez; Dulcenombre M. Saz-Navarro; A. Lopez-Fernandez; D. Rodríguez-Baena; F. Gómez-Vela Computational Ensemble Gene Co-Expression Networks for the Analysis of Cancer Biomarkers Journal Article In: Informatics, vol. 11, no. 2, pp. 14, 2024, ISSN: 2227-9709. @article{Figueroa-Martinez2024,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. |
A. Lopez-Fernandez; F. Gómez-Vela; J. González-Domínguez; P. Bidare-Divakarachari bioScience: A new python science library for high-performance computing bioinformatics analytics Journal Article In: SoftwareX, vol. 26, pp. 101666, 2024, ISSN: 2352-7110. @article{Lopez-Fernandez2024,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. |
Dulcenombre M. Saz-Navarro; A. Lopez-Fernandez; F. Gómez-Vela; D. Rodríguez-Baena CyEnGNet—App: A new Cytoscape app for the reconstruction of large co-expression networks using an ensemble approach Journal Article In: SoftwareX, vol. 25, pp. 101634, 2024, ISSN: 2352-7110. @article{Saz-Navarro2024,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. |
2021 |
A. Lopez-Fernandez; D. Rodríguez-Baena; F. Gómez-Vela; F. Divina; M. García-Torres A multi-GPU biclustering algorithm for binary datasets Journal Article In: Journal of Parallel and Distributed Computing, vol. 147, pp. 209-219, 2021, ISSN: 0743-7315. @article{Lopez-Fernandez2020,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. |
2020 |
A. Lopez-Fernandez; D. Rodríguez-Baena; F. Gómez-Vela gMSR: A Multi-GPU Algorithm to Accelerate a Massive Validation of Biclusters Journal Article In: Electronics, vol. 9, no. 11, pp. 1782, 2020, ISSN: 2079-9292. @article{Lopez-Fernandez2020b,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. |
B. Aram; A. Lopez-Fernandez; D. Muñiz-Amian The integration of heterogeneous information from diverse disciplines regarding persons and goods Journal Article In: Digital Scholarship in the Humanities, 2020. @article{Aram2020,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. |
2018 |
A. Lopez-Fernandez; D. Rodríguez-Baena; F. Gómez-Vela; N. Díaz-Díaz BIGO: A web application to analyse gene enrichment analysis results Journal Article In: Computational biology and chemistry, vol. 76, pp. 169-178, 2018, ISSN: 1476-9271. @article{Lopez-Fernandez2018,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. |
2017 |
F. Gómez-Vela; A. Lopez-Fernandez; J. A. Lagares; D. Rodríguez-Baena; C. D. Barranco; M. García-Torres; F. Divina Bioinformatics from a Big Data Perspective: Meeting the Challenge Conference IWBBIO 2017: Bioinformatics and Biomedical Engineering, pp. 349-359, Springer International Publishing, Cham, 2017, ISBN: 978-3-319-56154-7. @conference{Gómez-Vela2017,Recently, the rising of the Big Data paradigm has had a great impact in several fields. Bioformatics is one such field. In fact, Bioinfomatics had to evolve in order to adapt to this phenomenon. The exponential increase of the biological information available, forced the researchers to find new solutions to handle these new challenges. |