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{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.},

keywords = {Bioinformatics, Biomarkers, Breast cancer, Gene co-expression network, Prostate cancer, Stromal tissue},

pubstate = {published},

tppubtype = {article}

}

@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.},

keywords = {Bioinformatics, Data analysis, Data Mining, Data science, High-Performance Computing},

pubstate = {published},

tppubtype = {article}

}

@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.},

keywords = {Bioinformatics, Cytoscape, Gene networks, Network analysis, Visualisation},

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.},

keywords = {Bioinformatics, Biological validation, Gene enrichment analysis},

pubstate = {published},

tppubtype = {article}

}