publications
López-Garcı́a, G., Jerez, J. M., Ribelles, N., Alba, E., and Veredas, F. J. (2023). Explainable clinical coding with in-domain adapted transformers. Journal of Biomedical Informatics, 139:104323. DOI: 10.1016/j.jbi.2023.104323.
López-Garcı́a, G., Moreno-Barea, F. J., Mesa, H., Jerez, J. M., Ribelles, N., Alba, E., and Veredas, F. J. (2023). Named entity recognition for de-identifying real-world health records in spanish. In Mikyška, J., de Mulatier, C., Paszynski, M., Krzhizhanovskaya, V. V., Dongarra, J. J., and Sloot, P. M., editors, Computational Science ICCS 2023, pages 228–242, Cham. Springer Nature Switzerland. 10.1007/978-3-031-36024-4 17
Luque-Baena, R. M., Ortega-Zamorano, F., López-García, G., & Veredas, F. J. (2022). Wound Tissue Classification with Convolutional Neural Networks. In Artificial Intelligence in Healthcare and Medicine (pp. 197–222). https://doi.org/10.1201/9781003120902-8
Lopez-Garcia, G., Jerez, J. M., Ribelles, N., Alba, E., & Veredas, F. J. (2021). Transformers for Clinical Coding in Spanish. In IEEE Access (Vol. 9, pp. 72387–72397). https://doi.org/10.1109/access.2021.3080085
López-García, G., Jerez, J. M., Ribelles, N., Alba, E., & Veredas, F. J. (2021). Detection of Tumor Morphology Mentions in Clinical Reports in Spanish Using Transformers. In I. Rojas, G. Joya, & A. Catala (Eds.), Advances in Computational Intelligence (pp. 24–35). Springer International Publishing.
Urda, D., Veredas, F. J., González-Enrique, J., Ruiz-Aguilar, J. J., Jerez, J. M., & Turias, I. J. (2021). Deep neural networks architecture driven by problem-specific information. Neural Computing & Applications. https://doi.org/10.1007/s00521-021-05702-7
Veredas, F. J., Urda, D., Subirats, J. L., Cantón, F. R., & Aledo, J. C. (2020). Combining feature engineering and feature selection to improve the prediction of methionine oxidation sites in proteins. Neural Computing & Applications, 32(2), 323–334. https://doi.org/10.1007/s00521-018-3655-2
López-García, G., Jerez, J. M., Franco, L., & Veredas, F. J. (2020). Transfer learning with convolutional neural networks for cancer survival prediction using gene-expression data. PloS One, 15(3), e0230536. https://doi.org/10.1371/journal.pone.0230536
López-García, G., Jerez, J. M., Ribelles, N., Alba, E., & Veredas, F. J. (2020). ICB-UMA at CANTEMIST 2020: Automatic ICD-O Coding in Spanish with BERT. In M. Á. G. Cumbreras, J. Gonzalo, E. M. Cámara, R. M. Unanue, P. Rosso, S. J. Zafra, J. A. Ortiz-Zambrano, A. Miranda, J. Porta-Zamorano, Y. Guitiérrez, A. Rosá, M. Montes-y-Gómez, & M. García-Vega (Eds.), Proceedings of the Iberian Languages Evaluation Forum (IberLEF 2020) (pp. 468–476). CEUR Workshop Proceedings. http://ceur-ws.org/Vol-2664/cantemist_paper15.pdf
Lopez-Garcia, G., Jerez, J. M., Urda, D., & Veredas, F. J. (2019). MetODeep: A Deep Learning Approach for Prediction of Methionine Oxidation Sites in Proteins. In 2019 International Joint Conference on Neural Networks (IJCNN). https://doi.org/10.1109/ijcnn.2019.8851901
Urda, D., Veredas, F. J., Turias, I., & Franco, L. (2019). Addition of Pathway-Based Information to Improve Predictions in Transcriptomics. In I. Rojas, O. Valenzuela, F. Rojas, & F. Ortuño (Eds.), Bioinformatics and Biomedical Engineering. IWBBIO 2019 (Vol. 11466, pp. 200–208). Springer, Cham. https://doi.org/10.1007/978-3-030-17935-9_19
Urda, D., Aragón, F., Bautista, R., Franco, L., Veredas, F. J., Claros, M. G., & Jerez, J. M. (2018). BLASSO: integration of biological knowledge into a regularized linear model. BMC Systems Biology, 12(Suppl 5), 94. https://doi.org/10.1186/s12918-018-0612-8
Aledo, J. C., Cantón, F. R., & Veredas, F. J. (2017). A machine learning approach for predicting methionine oxidation sites. BMC Bioinformatics, 18(1), 430. https://doi.org/10.1186/s12859-017-1848-9
Urda, D., Aragón, F., Franco, L., Veredas, F. J., & Jerez, J. M. (2017). L1-regularization Model Enriched with Biological Knowledge. In I. Rojas & F. Ortuño (Eds.), Bioinformatics and Biomedical Engineering (Vol. 10208, pp. 579–590). Springer International Publishing. https://doi.org/10.1007/978-3-319-56148-6_52
Veredas, F. J., Cantón, F. R., & Aledo, J. C. (2017). Prediction of Protein Oxidation Sites. Advances in Computational Intelligence, 3–14. https://doi.org/10.1007/978-3-319-59147-6_1
Aledo J Carlos Cantón Francisco R Veredas, F. J. (2015). Sulphur Atoms from Methionines Interacting with Aromatic Residues Are Less Prone to Oxidation. Scientific Reports, 5(16955). https://doi.org/10.1038/srep16955
Veredas, F. J., Luque-Baena, R. M., Martín-Santos, F. J., Morilla-Herrera, J. C., & Morente, L. (2015). Wound image evaluation with machine learning. Neurocomputing, 164, 112–122. https://doi.org/10.1016/j.neucom.2014.12.091
Veredas, F. J., Mesa, H., & Morente, L. (2015). Efficient detection of wound-bed and peripheral skin with statistical colour models. Medical & Biological Engineering & Computing, 53(4), 345–359. https://doi.org/10.1007/s11517-014-1240-0
Morente, L., Morales-Asencio, J. M., & Veredas, F. J. (2014). Effectiveness of an e-learning tool for education on pressure ulcer evaluation. Journal of Clinical Nursing, 23(13-14), 2043–2052.
Veredas, F. J. (2014). Computational Intelligence for Pressure Ulcer Diagnosis. 24th Conference of the European Wound Management Association, EWMA-GNEAUPP.
Veredas, F. J., Ruiz-Bandera, E., Villa-Estrada, F., Rufino-González, J. F., & Morente, L. (2014). A web-based e-learning application for wound diagnosis and treatment. Computer Methods and Programs in Biomedicine, 116(3), 236–248.
Morente, L., & Veredas, F. J. (2013). ePULab An Adaptive e-Learning Tool for Pressure Ulcer Evaluation. Proceedings of the International Conference on Health Informatics, 155–160. https://doi.org/10.5220/0004187901550160
Navas, M., Luque-Baena, R. M., Morente, L., Coronado, D., Rodríguez, R., & Veredas, F. J. (2013). Computer-Aided Diagnosis in Wound Images with Neural Networks. In I. Rojas, G. Joya, & J. Cabestany (Eds.), Advances in Computational Intelligence (Vol. 7903, pp. 439–448). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-38682-4_47
Morente, L. (2012). Valoración de Úlceras por Presión Mediante Tecnologías de la Información y la Comunicación. Universidad de Málaga.
Morente, L., & Veredas, F. J. (2012). Validación de la herramienta de e-learning ePulab para la valoración de úlceras por presión. IX Simposio Nacional Sobre Ulceras Por Presión Y Heridas Crónicas.
Morente, L., Veredas, F. J., Mesa, H., & Morris, E. (2011). PULAB - Computational-Intelligence Aided Management, Diagnosis, Teleassistance and e-Learning of Pressure Ulcers. In V. Traver, A. L. N. Fred, J. Filipe, & H. Gamboa (Eds.), HEALTHINF (pp. 394–398). SciTePress. http://dblp.uni-trier.de/db/conf/biostec/healthinf2011.html#MorenteVMM11
Veredas, F. J., Mesa, H., & Morente, L. (2010). Binary tissue classification on wound images with neural networks and bayesian classifiers. IEEE Transactions on Medical Imaging, 29(2), 410–427.
Veredas, F. J., Morilla, J. C., & Morente, L. (2010). Predicting the Evolution of Pressure Ulcers. Proceedings of the Third International Conference on Health Informatics, 5–12. https://doi.org/10.5220/0002690700050012
Morente, L., Veredas, F. J., & Mesa, H. (2009a). Estimación Automática del Área de Úlceras por Presión mediante Técnicas de Visión e Inteligencia Computacional. I+S Informática Y Salud, 75, 105–108.
Morente, L., Veredas, F. J., & Mesa, H. (2009b). Estimación automática del área de úlceras por presión mediante técnicas de visión e inteligencia computacional. VI Congreso Nacional de Informática En Enfermería (INFORENF 2009), 128–129.
Veredas, F. J., Mesa, H., & Morente, L. (2009a). A hybrid learning approach to tissue recognition in wound images. International Journal of Intelligent Computing and Cybernetics, 2(2), 327–347. https://doi.org/10.1108/17563780910959929
Veredas, F. J., Mesa, H., & Morente, L. (2009b). Tissue Recognition Approach to Pressure Ulcer Area Estimation with Neural Networks. In J. Cabestany, F. Sandoval, A. Prieto, & J. Corchado (Eds.), Bio-Inspired Systems: Computational and Ambient Intelligence (Vol. 5517, pp. 1045–1052). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-02478-8_131
Mesa, H., Morente, L., Veredas, F., VanderSloten, J., Verdonck, P., Nyssen, M., & Haueisen, J. (2008). Tissue Recognition for Pressure Ulcer Evaluation. In J. Vander Sloten, P. Verdonck, M. Nyssen, & Haueisen. J. (Eds.), 4th European Conference of the International Federation For Medical and Biological Engineering (Vol. 22, pp. 1524–1527).
Mesa, H., Veredas, F. J., & Morente, L. (2008a). A hybrid approach for tissue recognition on wound images. 8th International Conference on Hybrid Intelligent Systems, HIS 2008, 120–125.
Mesa, H., Veredas, F. J., & Morente, L. (2008b). Tissue Recognition on Pressure Ulcer Images in Non Controlled Environments. International Conference on Visualization, Imaging and Image Processing, VIIP’2008, 35–40.
Veredas, F. J., Mesa, H., & Martínez, L. A. (2008). Imprecise correlated activity in self-organizing maps of spiking neurons. Neural Networks: The Official Journal of the International Neural Network Society, 21(6), 810–816.
Veredas, F. J., Martínez, L. A., & Mesa, H. (2007). Self-organizing Maps of Spiking Neurons with Reduced Precision of Correlated Firing. In J. de Sá, L. Alexandre, W. Duch, & D. Mandic (Eds.), Artificial Neural Networks – ICANN 2007 (Vol. 4669, pp. 349–358). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-540-74695-9_36
Veredas, F. J., Rodríguez, J. M., Mesa, H., & Morente, L. (2007). Self-organizing maps to measure the contour of skin pressure ulcers in digital images. Third International Conference on Computational Intelligence in Medicine and Healthcare (CIMED2007).
Mesa, H., & Veredas, F. J. (2007). Integrate-and-Fire Neural Networks with Monosynaptic-Like Correlated Activity. In J. de Sá, L. Alexandre, W. Duch, & D. Mandic (Eds.), Artificial Neural Networks – ICANN 2007 (Vol. 4668, pp. 539–548). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-540-74690-4_55
Veredas, F. J., & Mesa, H. (2006a). Modelo de conductancia sináptica para el análisis de la correlación de actividad entre neuronas de integración y disparo. Campus Multidisciplinar de Percepción E Inteligencia (CMPI 2006), 207–218.
Veredas, F. J., & Mesa, H. (2006b). Optimized synaptic conductance model for integrate-and-fire neurons. 10th IASTED International Conference Artificial Intelligence and Soft Computing, 97–102.
Veredas, F. J., Vico, F. J., & Alonso, J. M. (2006). Evolving Networks of Integrate-And-Fire Neurons. Neurocomputing, 69(13-15), 1561–1569.
Morente, L., Ruiz, E., Soldado, A., & Veredas, F. J. (2005). ITACA: designing pressure ulcer assessment models by means of telematic clinical data processing. I International Congress on Community Nursing.
Sánchez, A., Vico, F., Cabello, S., Veredas, F., Seamari, Y., López, I., Farfán, J., & García-Herrera, G. (2005). A Competitive-Based Method for Determining the Number of Groups: A Clinical Application. In J. Cabestany, A. Prieto, & F. Sandoval (Eds.), Computational Intelligence and Bioinspired Systems (Vol. 3512, pp. 1214–1221). Springer Berlin Heidelberg. https://doi.org/10.1007/11494669_149
Veredas, F. J., Vico, F. J., & Alonso, J. M. (2005a). Estudio de la influencia de factores fisiologicos y de conectividad de red en la correlacion de actividad entre pares de neuronas de integracion y disparo. Inteligencia Artificial, Revista Iberoamericana de Inteligencia Artificial, 9(27), 115–118. http://polar.lsi.uned.es/revista/index.php/ia/article/view/463
Veredas, F. J., Vico, F. J., & Alonso, J.-M. (2005b). Factors determining the precision of the correlated firing generated by a monosynaptic connection in the cat visual pathway. The Journal of Physiology, 567(Pt 3), 1057–1078.
Vico, F. J., Veredas, F. J., Seamari, Y., & López, I. (2005). Formalización y aplicación de un proceso de especiación biológica. I Congreso Español de Informática. IV Congreso Español Sobre Metaheurísticas, Algoritmos Evolutivos Y Bioinspirados, 475–477.
Veredas, F. J. (2004). A web-based simulator of spiking neurons for correlated activity analysis. Lecture Series on Computer and Computational Sciences, Volume 1, 539–542.
Veredas, F. J., Alonso, J. M., & Vico, F. J. (2004). Estudio de la influencia de factores fisiológicos y de conectividad de red en la correlación de actividad entre pares de neuronas de integración y disparo. I Simposio de La Red Andaluza de Sistemas Inteligentes (ISTANET), 163–184.
Veredas, F. J., Vico, F. J., & Alonso, J. M. (2004). A computational tool to simulate correlated activity in neural circuits. Journal of Neuroscience Methods, 136(1), 23–32.
Vico, F. J., Mir, P., Veredas, F. J., & De La Torre, J. (2001). Animal-like adaptive behavior. Artificial Intelligence in Engineering, 15(1), 5–12.
Vico, F. J., Guillén, M. J., Veredas, F. J., & Polifeme, C. (2000). Modeling the intrinsic dynamics of biological speciation. Real Life Evolutionary Design Optimization. PPSN’2000, 138–140.
Veredas, F. J., Alonso, J. M., & Vico, F. J. (2000). Exploring networks that generate correlated firing consistent with monosynaptic connections. Society For Neuroscience Abstracts, 26(1-2), Abstract No. – 736.10.
Veredas, F. J., Vico, F. J., & Guillen, M. J. (2000). Genetic algorithms with implicit encoding for solving high-dimensional optimization problems. Real Life Evolutionary Design Optimization. PPSN’2000, 151–155.
Vico, F. J., Guillén, M. J., Burrezo, S., Aguilar, J., Polifeme, C., & Veredas, F. J. (2000). A clustering method based on genetic search and competitive learning. 7th International Conference on Neural Information Processing, 750–754.
Manzano, O. R., Veredas, F., Guillen, M. J., Vico, F. J., & Roman, J. (2000). Inhibitory unlearning: a mechanism for increasing the storage capacity in an attractor network. KES’2000. Fourth International Conference on Knowledge-Based Intelligent Engineering Systems and Allied Technologies. Proceedings (Cat. No.00TH8516), 1.
Torre, J., Vico, F. J., & Veredas, F. (1999a). A Clustering Algorithm Based on Competitive Learning. In C. Dagli (Ed.), Intelligent Engineering Systems Through Artificial Neural Networks, 9 (Smart Engineering System Design) (pp. 765–770). ASME Press, NY.
Torre, J., Vico, F. J., & Veredas, F. (1999b). Automatic Measurements on Knee Implant X-ray Images. In C. Dagli (Ed.), Intelligent Engineering Systems Through Artificial Neural Networks, 9 (Smart Engineering System Design) (pp. 969–974). ASME Press, NY.
Vico, F. J., Veredas, F. J., Bravo, J. M., & Almaraz, J. (1999). Automatic design synthesis with artificial intelligence techniques. Artificial Intelligence in Engineering, 13(3), 251–256.
Veredas, F., & Vico, F. (1998). Computación evolutiva basada en un modelo de codificación implícita. Iberoamerican Journal of Artificial Intelligence, 2(5), 20–25.
Bandera, C., Du, F., Bravo, J. M., Veredas, F., Vico, F. J., & Ortega, F. (1997). Pork carcasses characterization with color vision. European Simulation Multiconference, 359–366.
Redrejo, A., Veredas, F. J., Vico, F. J., & Rodríguez, O. (1997). Optimización de trayectorias de reparto mediante algoritmos genéticos. II Jornadas de Transferencia Tecnológica de Inteligencia Artificial, 25–34.
Veredas, F. J., Vico, F. J., Bravo, J. M., Redrejo, A., & Almaraz, J. (1997). Artificial intelligence techniques for automatic industrial design. II Jornadas de Transferencia Tecnológica de Inteligencia Artificial, 53–58.
Vico, F. J., Bravo, J. M., Veredas, F. J., Ortega, F., & Mir, P. (1997). A desing synthesis paradigm based on neural and genetic systems. Concurrent Engineering European Conference, 149–153.
Vico, F. J., Bravo, J. M., Veredas, F., Redrejo, A., & Ortega, F. (1997). Neural and Evolutionary Computation for Industrial Manufacturing. European Simulation Multiconference, 81–86.
Vico, F. J., Mir, P., Montañez, M., Veredas, F., & Almaraz, J. (1997). Animal Learning for Adaptive User Interfaces. II Jornadas de Transferencia Tecnológica de Inteligencia Artificial, 17–24.