Alfredo Vellido / publications

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2024

C König and A Vellido. Exploring data distributions in Machine Learning models with SOMs. In International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization (WSOM+ 2024). Accepted for publication.

G Ungan, A Pons-Escoda, D Ulinic, C Arús, S Ortega-Martorell, I Olier, A Vellido, C Majós, M Julià-Sapé. Early pseudoprogression and progression lesions in glioblastoma patients are both metabolically heterogeneous. NMR in Biomedicine, 37(4):e5095 https://doi.org/10.1002/nbm.5095

C Pitarch, G Ungan, M Julià-Sapé, A Vellido. Advances in the Use of Deep Learning for the Analysis of Magnetic Resonance Image in Neuro-Oncology. Cancers, 14(2): 300. open access.

2023

G Ungan, A Pons-Escoda, D Ulinic, C Arús, S Ortega-Martorell, A Vellido, C Majós, M Julià-Sapé. Metabolic pattern recognition of contrast-enhancing lesions in glioblastomas one month after concomitant therapy. NMR in Biomedicine, accepted.

JM López-Correa, C König, A Vellido. GPCR molecular dynamics forecasting using recurrent neural networks. Scientific Reports, 13:20995

M Hueso, R Álvarez, D Marí, V Ribas-Ripoll, K Lekadir, A Vellido. Is generative artificial intelligence the next step toward a personalized hemodialysis? Revista de Investigación Clínica - Clinical and Translational Investigation. doi: 10.24875/RIC.23000162. Epub ahead of print

A Benali, R Martin-Pinardel, E Romero, A Vellido. Optimización de Imágenes. En Inteligencia Artificial y Datos de Imagen. Procesamiento de Imágenes (capitulo). Inteligencia Artificial y Oftalmología: Estado Actual en Cataluña. Annals d’Oftalmologia 31(4):198-205

M Hueso, N Rotllan, JC Escolà-Gil, and A Vellido. Editorial: Systems biology and data-driven machine learning-based models in personalized cardiovascular medicine. Frontiers in Cardiovascular Medicine, 10:1320110. doi: 10.3389/fcvm.2023.1320110

M Hueso, A Valencia, J Carbonell, R Álvarez and A Vellido. Complex Data Representations, Modeling and Computational Power for a Personalized Dialysis. In C.P. Sharma, T. Chandy, V. Thomas (eds.) Artificial Intelligence in Tissue and Organ Regeneration. Academic Press, pp.219-236

M Hueso, RI Rodríguez Urquía, R Alvarez Esteban, M Quero, GE Villalobos, C Galofre, R Peiró-Jordán, DM Martínez, A Vellido. Empowering Dialysis Patients to Self-Manage Hyperphosphatemia with Mobile Health Technology: Insights from the FOSFO-OK study. American Society of Nephrology (ASN) Kidney Week Meeting, 2023.

G Ungan, C Arús, A Vellido, M Julià-Sapé. A Comparison of Non-Negative Matrix Underapproximation Methods for the Decomposition of Magnetic Resonance Spectroscopy Data from Human Brain Tumors. NMR in Biomedicine, 36(12):e5020, open access

C Pitarch, V Ribas, A Vellido. AI-Based Glioma Grading for a Trustworthy Diagnosis: An Analytical Pipeline for Improved Reliability. Cancers, 15(13), 3369.

G Ungan, A Pons-Escoda, D Ulinic, C Arús, A Vellido, M Julià-Sapé. Using Single-Voxel Magnetic Resonance Spectroscopy Data Acquired at 1.5T to Classify Multivoxel Data at 3T: A Proof-of-Concept Study. Cancers, 15(14), 3709.

PJG Lisboa, S Saralajew, A Vellido, R Fernández-Domenech, T Villmann. The Coming of Age of Interpretable and Explainable Machine Learning Models. Neurocomputing, 535, 25-39, doi

G Ungan, A Pons, D Ulinic, C Arús, A Vellido, M Julià-Sapé. Nosological images of brain tumor MV-MRS 3T data based on classifiers trained with SV-MRS 1.5T data, a proof-of-concept. Annual Meeting of the International Society for Magnetic Resonance in Medicine (ISMRM'23)

MA Gutiérrez Mondragón, C König, A Vellido. Recognition of conformational states of a G Protein-Coupled Receptor from molecular dynamic simulations using sampling techniques. In I Rojas, O Valenzuela, F Rojas, LJ Herrera and F Ortuño (Eds.)  Procs. of the 10th International Work-Conference on Bioinformatics and Biomedical Engineering, Part I,  IWBBIO 2023, LNBI 13919, Springer, pp.3-16.

MA Gutiérrez Mondragón, C König, A Vellido, Layer-wise Relevance Analysis for Motif Recognition in the Activation Pathway of the β2-adrenergic GPCR Receptor. International Journal of Molecular Sciences, 24(2): 1155.

L Carrera-Escalé, A Benali, A-C Rathert, R Martín-Pinardel, A Alé-Chilet, M Barraso, C Bernal-Morales, S Marín-Martinez, S Feu-Basilio, J Rosinés-Fonoll, T Hernandez, I Vilá, C Oliva, I Vinagre, E Ortega, M Gimenez, E Esmatjes, A Vellido, E Romero and J Zarranz-Ventura. Radiomics-Based Assessment of OCT Angiography Images for Diabetic Retinopathy Diagnosis, Ophthalmology Science, 3(2), 100259. doi.org/10.1016/j.xops.2022.100259

2022

A Vellido, V Ribas, AI and ICU Monitoring on the Wake of the COVID-19 Pandemic, In: N Lidströmer, YC Eldar (eds.) Artificial Intelligence in Covid-19, Springer, pp.169-174. https://doi.org/10.1007/978-3-031-08506-2

J Zarranz-Ventura, L Carrera-Escale, A Benali, A-C Rathert, R Martín-Pinardel, A Ale-Chilet, M Barraso, C Bernal-Morales, S Marín-Martínez, S Feu-Basilio, J Rosines-Fonoll, T Hernández, I Vila, R Castro, C Oliva, I Vinagre, E Ortega, M Giménez, A Vellido, E Romero. Radiomics-based assessment of multimodal retinal imaging techniques for diabetes mellitus and diabetic retinopathy diagnosis. In: 22nd EURETINA Congress, Hamburg, Germany (abstract)

JM López Correa, C König, A Vellido, Molecular Dynamics forecasting of transmembrane Regions in GPCRs by Recurrent Neural Networks, 2022 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI), pp.1-4, doi: 10.1109/BHI56158.2022.9926945.

JM López Correa, C König, A Vellido, Long Short-Term Memory to predict 3D Amino acids Positions in GPCR Molecular Dynamics, In Artificial Intelligence Research and Development, Proceedings of the 24th International Conference of the Catalan Association for Artificial Intelligence  CCIA 2022, Frontiers in Artificial Intelligence and Applications series, IOS Press, pp.209-220.

D Bacciu, PJG Lisboa, A Vellido (Eds) Deep Learning in Biology and Medicine, World Scientific, 2022.

D Bacciu, PJG Lisboa, A Vellido. Introduction. In: D Bacciu, PJG Lisboa, A Vellido (Eds) Deep Learning in Biology and Medicine, World Scientific, pp.1-10, 2022.

A. Vellido, P.J.G. Lisboa, J.D. Martín, The Need for Interpretable and Explainable Deep Learning in Medicine and Healthcare. In: D Bacciu, PJG Lisboa, A Vellido (Eds) Deep Lerning in Biology and Medicine, World Scientific, pp.247-264, 2022.

Y Hernández-Villegas, S. Ortega-Martorell, C. Arús, A. Vellido, M. Julià-Sapé. Extraction of artefactual MRS patterns from a large database using  non-negative matrix factorization. NMR in Biomedicine; 35(4):e4193. https://doi.org/10.1002/nbm.4193

S Sánchez-Martínez, O Camara, G Piella, M Cikes, MA González Ballester, M Miron, A Vellido, E Gómez Gutiérrez, AG Fraser and B Bijnens. Machine learning for clinical decision-making: challenges and opportunities in cardiovascular imaging. Frontiers in Cardiovascular Medicine - Cardiovascular Imaging, 8, p2020.

GS Ungan, A Pons Escoda, D Ulinic, C. Arús, A Vellido, C Majós and M Julià-Sapé, MRSI-detected pattern in glioblastoma patients one month after concomitant chemoradiotherapy, Proceeding of the International Society for Magnetic Resonance in Medicine, ISMRM, 30, 0840.

A Benali, L Carrera, A Christin, R Martín, A Alé, M Barraso, C Bernal, S Marín, S Feu, J Rosinés, T Hernández, I Vilá, C Oliva, I Vinagre, E Ortega, M Giménez, E Esmatjes, J Zarranz-Ventura, E Romero, A Vellido. NMF for quality control of multi-modal retinal images for diagnosis of diabetes mellitus and diabetic retinopathy, In I Rojas, O Valenzuela, F Rojas, LJ Herrera and F Ortuño (Eds.)  Procs. of the 9th International Work-Conference on Bioinformatics and Biomedical Engineering, Part I,  IWBBIO 2022, LNBI 13346, Springer, pp.343-356.

MA Gutiérrez-Mondragón, C König, A Vellido. A Deep Learning-based method for uncovering GPCR ligand-induced conformational states using interpretability techniques, In I Rojas, O Valenzuela, F Rojas, LJ Herrera and F Ortuño (Eds.)  Procs. of the 9th International Work-Conference on Bioinformatics and Biomedical Engineering, Part I,  IWBBIO 2022, LNBI 13346, Springer, pp. 275-287.

S Cavallaro, A Vellido, C König. Visual insights from the latent space of generative models for molecular design, In Advances in Self-Organizing Maps, Learning Vector Quantization, Clustering and Data Visualization. Dedicated to the Memory of Teuvo Kohonen / Proceedings of the 14th International Workshop, WSOM+ 2022, pp108-117.

A Vellido, C Angulo, K Gibert A self-organizing world: special issue of the 13th edition of the workshop on self-organizing maps and learning vector quantization, clustering and data visualization, WSOM+ 2019, Neural Computing & Applications, 34, 1-3, DOI:10.1007/s00521-021-06307-w

S Behrouzieh, M Keshavarz-Fathi, A Vellido, S Seyedpour, S Adiban, A Vahed, T Dorigo and N Rezaei. Ethical Deliberation on AI-Based Medicine. In: Rezaei, N. (eds) Multidisciplinarity and Interdisciplinarity in Health. Integrated Science, vol 6, pp.567-592. Springer, Cham. https://doi.org/10.1007/978-3-030-96814-4_25

N Rezaei, A Saghazadeh, AR Izaini Ghani, A Vedadhir, A Vahed, A Vellido, et al. Integrated Science 2050: Multidisciplinarity and Interdisciplinarity in Health. In: Rezaei, N. (eds) Multidisciplinarity and Interdisciplinarity in Health. Integrated Science, vol 6. Springer, Cham, pp.661-690. https://doi.org/10.1007/978-3-030-96814-4_30

A Vellido, V Ribas, Artificial Intelligence in Critical Care: The Path From Promise to Practice. In N Lidströmer and H Ashrafian (eds) Artificial Intelligence in Medicine. Springer Nature, Reference series, ch.106, pp.1469-1478  (link)

2021

AX Astudillo Aguilar, S Rosso, K Gibert, A Vellido, Visual mining of industrial gas turbines sensor data as an industry 4.0 application. In 16th International Conference on Soft Computing Models in Industrial and Environmental Applications, SOCO 2021, H Sanjurjo González et al. (eds) Advances in Intelligent Systems and Computing, vol.1401, Springer, pp.101-111.

P Lisboa, S Saralajew, A Vellido and T Villmann. The coming of age of interpretable and explainable machine learning models. In Proceedings of the 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2021)  pp.547-556. (link)

A Vellido, The importance of interpretability and visualization in ML for medical applications, II IABiomed Workshop, CEDI 2021, CAEPIA, Málaga

2020

LM Núñez, E Romero, M Julià-Sapé, MJ Ledesma-Carbayo, A Santos, C Arús, AP Candiota and A Vellido, A. Unraveling response to temozolomide in preclinical GL261 glioblastoma with MRI/MRSI using radiomics and signal source extraction. Scientific Reports 10:19699. https://doi.org/10.1038/s41598-020-76686-y

LM Núñez, M Julià-Sapé, E Romero, C Arús, A Vellido and AP Candiota. Monitoring of TMZ therapy in murine GL261 glioblastoma by combining Radiomics, Convex-NMF and feature selection in MRI/MRSI data analysis. L01.78 in ESMRMB 2020 Online, 37th Annual Scientific Meeting, September 30–October 2: Lightning Talks / Electronic Posters / Clinical Review Posters / Software Exhibits. Magnetic Resonance Materials in Physics, Biology and Medicine 33, 69–233. https://doi-org.recursos.biblioteca.upc.edu/10.1007/s10334-020-00876-y

LM Núñez, M Julià-Sapé, E Romero, C Arús, A Vellido, AP Candiota. Interpreting response to TMZ therapy in murine GL261 glioblastoma by combining Radiomics, Convex-NMF and feature selection in MRI/MRSI data analysis. In 2020 International Joint Conference on Neural Networks (IJCNN), Glasgow, United Kingdom, pp. 1-8, doi: 10.1109/IJCNN48605.2020.9207031

A Vellido. The importance of interpretability and visualization in Machine Learning for applications in medicine and health care. Neural Computing & Applications 32, 18069-18083. draft.

2019

S Sánchez-Martínez, O Camara, G Piella, M Cikes, MA Gonzalez Ballester, M Miron, A Vellido, E Gómez, A Fraser, B Bijnens. Machine Learning for Clinical Decision-Making: Challenges and Opportunities. Preprints, 2019110278 (doi: 10.20944/preprints201911.0278.v1)

A Bazaga, Y Ofir-Rosenfeld, O Rausch, A Vellido, H Weisser, J Sullivan. Genome-wide investigation of gene-cancer associations using machine learning on biomedical big data for the prediction of novel therapeutic targets. In ISMB/ECCB 2019.

A Bazaga, A Vellido. Network Community Cluster-Based Analysis for the Identification of Potential Leukemia Drug Targets. In Procs. of the International Workshop on Self-Organizing Maps (WSOM+19), pp. 314-323. Springer.

D Bacciu, B Biggio, PJG Lisboa, JD Martín, L Oneto, A  Vellido.  Societal issues in Machine Learning: when learning from data is not enough. In Proceedings of the 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2019), Bruges, Belgium, pp.455-464.

V Ribas Ripoll, A Vellido. Blood pressure assessment with differential pulse transit time and deep learning: a proof of concept. Kidney Diseases, 5(1): 23-27. DOI: 10.1159/000493478

M Hueso A Vellido. Artificial Intelligence and Dialysis. Kidney Diseases, 5(1): 1-2. DOI: 10.1159/000493933

A Vellido. Societal Issues Concerning the Application of Artificial Intelligence in Medicine. Kidney Diseases, 5(1): 11-17. free access https://doi.org/10.1159/000492428.

2018

A Bilal, A Vellido, V Ribas. Enabling interpretation of the outcome of a human obesity prediction machine learning analysis from genomic data. In procs. of the 15th International Conference on Computational Intelligence methods for Bioinformatics and Biostatistics (CIBB 2018) Lisbon, Portugal.

C König, I Shaim, A Vellido, E Romero, R Alquézar, J Giraldo. Using machine learning tools for protein database biocuration assistance, Scientific Reports, 8:10148.

A Aushev, V Ribas Ripoll, A Vellido, F Aletti, B Bollen Pinto, A Herpain, E Hendrik Post, E Romay Medina, R Ferrer, G Baselli, K Bendjelid. Feature selection for the accurate prediction of septic and cardiogenic shock ICU mortality in the acute phase. PLoS ONE, 13(11): e0199089. https://doi.org/10.1371/journal.pone.0199089.

D Bacciu, PJ Lisboa, JD Martín, R Stoean, and A Vellido. Bioinformatics and medicine in the era of Deep Learning. In Proceedings of the 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2018), Bruges, Belgium, pp.345-354.

A Bilal, A Vellido, V Ribas. Big data analytics for obesity prediction. 21st International Conference of the Catalan Association for Artificial Intelligence (CCIA 2018) Roses, Spain. In Falomir, Z., Gibert, K. and Plaza, E., eds. Frontiers in Artificial Intelligence and Applications, vol.308, pp.141-145, IOS Press.

M Hueso, A Vellido, N Montero, C Barbieri, R Ramos, M Angoso, JM Cruzado, A Jonsson. Artificial Intelligence for the artificial kidney: pointers to the future of a personalized hemodialysis therapy. Kidney Diseases; 4:1-9. open access.

A Vellido, V Ribas, L Subirats, C Morales, A Ruiz Sanmartín, JC Ruiz Rodríguez. Machine Learning for critical care: state-of-the-art and a sepsis case study, BioMedical Engineering OnLine, 17(S1):135.

C König, R Alquézar, A Vellido, J Giraldo J. Systematic analysis of primary sequence domain segments for the discrimination between class C GPCR subtypes, Interdisciplinary Sciences: Computational Life Sciences, 10(1), 43-52.

2017

Y Hernández–Villegas, V Mocioiu, D Ulinic, SP Kyathanahally, A Vellido, C Arús, M Julià-Sapé. Automated quality control of magnetic resonance spectra of brain tumors by Convex Non-negative Matrix Factorization. In 34th Annual Scientific Meeting of the European Society for Magnetic Resonance in Medicine and Biology (ESMRMB), Barcelona, Spain.

A Shkurin, A Vellido, Using random forests for assistance in the curation of G-protein coupled receptor databases. BioMedical Engineering OnLine, 16(Suppl 1):75

C König, R Alquézar, A Vellido, J Giraldo. Discovering subtype specific n-gram motifs in class C GPCR N-termini. In Recent Advances in Artificial Intelligence Research and Development: Proceedings of the 20th International Conference of the Catalan Association for Artificial Intelligence, Deltebre, Terres de L'Ebre, Spain, October 25-27, 2017 (Vol. 300, p. 116). IOS Press.

A Vellido, DL García. Electricity rate planning for the current consumer market scenario through segmentation of consumption time series. In Artificial Intelligence in Power and Energy Systems (AIPES) 18th EPIA Conference on Artificial Intelligence, Porto, Portugal, LNCS 10423, pp. 295-306, Springer

DL García, À Nebot, A Vellido. Intelligent data analysis approaches to churn as a business problem: a survey. Knowledge and Information Systems, 51(3):719-774. doi: 10.1007/s10115-016-0995-z (draft)

X Cipriano, A Vellido, J Cipriano, J Martí, S Danov. Application of clustering and simulation methods for the analysis of socio-economical and technical factors influencing the energy refurbishment of neighbourhoods. Energy Efficiency, 10(2), 359-382. doi: 10.1007/s12053-016-9460-9

C König, R Alquézar, A Vellido, J Giraldo. Topological sequence segments discriminate between class C GPCR subtypes. In Procs. of the 11^th International Conference on Practical Applications of Computational Biology & Bioinformatics (PACBB 2017), Porto, Portugal, 164-172.

A Vellido, V Ribas, C Morales, A Ruiz Sanmartín, JC Ruiz-Rodríguez. Machine Learning for Critical Care: An Overview and a Sepsis Case Study. In I. Rojas and F. Ortuño (Eds.): Procs. of the 5^th International Work-Conference on Bioinformatics and Biomedical Engineering (IWBBIO 2017), Granada, Spain. Part I, LNBI 10208, pp.15-30, Springer. doi: 10.1007/978-3-319-56148-6 2.

2016

C Morales, A Vellido, V Ribas. Applying Conditional Independence Maps to improve Sepsis Prognosis. Data Mining in Biomedical Informatics and Healthcare (DMBIH) Workshop. IEEE International Conference on Data Mining (ICDM 2016)

JRG Cárdenas, À Nebot, F Mugica, A Vellido. A decision making support tool: The Resilience Management Fuzzy Controller, In 2016 IEEE Congress on Evolutionary Computation (CEC / WCCI'16), pp.2313-2320.

I Paz, À Nebot, E Romero, F Mugica and A Vellido: A methodological approach for algorithmic composition systems' parameter spaces aesthetic exploration,  In 2016 IEEE Congress on Evolutionary Computation (CEC / WCCI'16), pp.1317-1323.

E Racec, S Budulan, A Vellido. Computational Intelligence in architectural and interior design: a state-of-the-art and outlook on the field. In Artificial Intelligence Research and Development: Proceedings of the 19th International Conference of the Catalan Association for Artificial Intelligence (CCIA 2016), Barcelona, Spain, Vol. 288, p.108. IOS Press. [draft]

MI Cárdenas, A Vellido J Giraldo, Visual exploratory assessment of class C GPCR extracellular domains discrimination capabilities, In Procs. of the 10th International Conference on Practical Applications of Computational Biology & Bioinformatics (PACBB'16) Advances in Intelligent Systems and Computing series, Vol.477, Springer, pp.31-39.

JD Martín-Guerrero, JPG Lisboa, A Vellido, Physics and Machine Learning: emerging paradigms. In Proceedings of the 24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2016), Bruges, Belgium, pp.319-326. [draft]

V Mocioiu, NM Pedrosa de Barros, S Ortega-Martorell, J Slotboom, U Knecht, C Arús, A Vellido M Julià-Sapé. A machine learning pipeline for supporting differentiation of glioblastomas from single brain metastases, In Proceedings of the 24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2016), Bruges, Belgium, pp.247-252. .

A Vilamala, A Vellido, L Belanche. Bayesian Semi Non-negative Matrix Factorisation. In Proceedings of the 24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2016), Bruges, Belgium. pp.195-200.

V Mocioiu, SP Kyathanahally, C Arús, A Vellido, M Julià-Sapé. Automated quality control for proton magnetic resonance spectroscopy data using convex non-negative matrix factorization,  In Bioinformatics and Biomedical Engineering (F. Ortuño, I.Rojas, eds.) Proceedings of the 4th International Conference, IWBBIO 2016, Granada, Spain, April 20-22, 2016, LNCS/LNBI 9656, pp 719-727

A Shkurin, A Vellido, Random Forests for quality control in G-Protein Coupled Receptor databases, In Bioinformatics and Biomedical Engineering (F. Ortuño, I.Rojas, eds.) Proceedings of the 4th International Conference, IWBBIO 2016, Granada, Spain, April 20-22, 2016, LNCS/LNBI 9656, pp 707-718.

2015

CL König, MI Cárdenas, J Giraldo, R Alquezar, A Vellido. Label noise in subtype discrimination of class C G-protein coupled receptors: A systematic approach to the analysis of classification errors. BMC Bioinformatics, 16(1):314. open access

PJG Lisboa, JD Martín, A Vellido. Making nonlinear manifold learning models interpretable: the Manifold Grand Tour. Expert Systems with Applications, 42(22), 8982-8988.

MI Cárdenas, A Vellido, C König, R Alquézar J Giraldo, Visual characterization of misclassified class C GPCRs through manifold-based Machine Learning methods. Genomics and Computational Biology, 1(1), e19. open access.

C König, R Alquézar, A Vellido, J Giraldo. The extracellular N-terminal domain suffices to discriminate class C G Protein-Coupled  Receptor subtypes from n-grams of their sequences, In: Procs. of the International Joint-Conference on Artificial Neural Networks (IJCNN 2015), Killarney, Ireland, pp.2330-2336.

A Vellido, C Halka, À Nebot. A weighted Cramer's V index for the assessment of stability in the fuzzy clustering of class C G Protein-Coupled Receptors. In F. Ortuño and I. Rojas (Eds.): IWBBIO 2015, Part I, LNCS 9043, pp. 536--547, Springer

R Cruz-Barbosa, A Vellido, J Giraldo. The influence of alignment-free sequence representations on the semi-supervised classification of Class C G Protein-Coupled Receptors. Medical & Biological Engineering & Computing, 53(2), 137-149, available online

2014

C König, R Alquézar, A Vellido, J Giraldo. Finding class C GPCR subtype-discriminating n-grams through feature selection. Journal of Integrative Bioinformatics, 11(3):254. doi: 10.2390/biecoll-jib-2014-254.

A Tosi, A Vellido. Probabilistic Geometries as a tool for Interpretability in Dimensionality Reduction Models, In the 8th WiML Workshop, Advances in Neural Information Processing Systems (NIPS 2014), Montreal, Canada.

A Tosi, S Hauberg, A Vellido, N Lawrence. Metrics for probabilistic geometries, In  Nevin L. Zhang, Jin Tian (eds.) Proceedings of The 30th Conference on Uncertainty in Artificial Intelligence (UAI 2014), AUAI Press Corvallis, Oregon, USA, pp.800-808.

MI Cárdenas, A Vellido, J Giraldo. Exploratory visualization of Metabotropic Glutamate Receptor subgroups through manifold learning. 17^th International Conference of the Catalan Association of Artificial Intelligence (CCIA 2014) In L. Museros et al. (Eds.) Artificial Intelligence Research and Development, IOS Press, pp.269-272.

A Vilamala, Ll Belanche, A Vellido. A MAP approach for Convex Non-negative Matrix Factorization in the Diagnosis of Brain Tumors. 4^th International Workshop on Pattern Recognition in Neuroimaging (PRNI 2014) pp.1-4 doi: 10.1109/PRNI.2014.6858550

A Tosi, A Vellido. Local metric and graph based distance for probabilistic dimensionality reduction. The workshop on Features and Structures (FEAST 2014)  International Conference on Pattern Recognition (ICPR 2014), Stockholm, Sweden. (best poster award)

C König, R Alquézar, A Vellido. Finding class C GPCR subtype-discriminating  n-grams through feature selection, In Procs. of the 8^th International Conference on Practical Applications of Computational Biology & Bioinformatics (PACBB 2014), pp.89-96.

MI Cárdenas, A Vellido, J Giraldo, Visual interpretation of class C GPCR subtype overlapping from the nonlinear mapping of transformed primary sequences. In Procs. of the 2^nd International Conference on Biomedical and Health Informatics (IEEE BHI'14)  pp.764-767.

VJ Ribas Ripoll, A Wojdel, A Sáez de Tejada Cuenca, JC Ruiz-Rodríguez, A Ruiz-Sanmartín, M de Nadal, E Romero, A Vellido, Continuous blood pressure assessment from a photoplethysmographic signal with Deep Belief Networks, The FASEB Journal, 28(1), Supplement LB674.

V Ribas, A Vellido, E Romero, JC Ruiz-Rodríguez. Sepsis Mortality Prediction with Quotient Basis Kernels. Artificial Intelligence in Medicine, 61(1), 45-52. DOI:10.1016/j.artmed.2014.03.004

MI Cárdenas, A Vellido, C König, R Alquézar J Giraldo, Exploratory visualization of misclassified GPCRs from their transformed unaligned sequences using manifold learning techniques, In F. Ortuño, I. Rojas (eds.): Procs. of the 2^nd International Work-Conference on Bioinformatics and Biomedical Engineering (IWBBIO 2014) pp.623-630.

A Tosi, I Olier, A Vellido. Probability ridges and distortion flows: Visualizing multivariate time series using a variational Bayesian manifold learning method. 10th Workshop on Self-Organizing Maps (WSOM 2014). Advances in Intelligent Systems and Computing, Vol.295, pp.55-64.

C Arizmendi, DA Sierra, A Vellido E Romero. Automated classification of brain tumours from short echo time in vivo MRS data using Gaussian decomposition and Bayesian Neural Networks, Expert Systems with Applications, 41(11), 5296-5307. doi: http://dx.doi.org/10.1016/j.eswa.2014.02.031.

C König, A Vellido, R Alquézar, J Giraldo. Misclassification of class C G-protein-coupled receptors as a label noise problem. In Proceedings of the 22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2014), Bruges, Belgium. pp. 695-700.
 

2013

S Ortega-Martorell, H Ruiz, A Vellido, I Olier, E Romero, M Julià-Sapé, JD Martín, IH Jarman, C Arús, PJG Lisboa. A novel semi-supervised methodology for extracting tumor type-specific MRS sources in human brain data, PLoS ONE, 8(12), 2013: e83773

A Vilamala, PJG Lisboa, S Ortega-Martorell, A Vellido. Discriminant Convex Non-negative Matrix Factorization for the Classification of Human Brain Tumours, Pattern Recognition Letters, 34(14), 1734–1747.

R Cruz-Barbosa, A Vellido. Generative manifold learning for the exploration of partially labeled data. Computación & Sistemas, 17(4), 641-653. doi: 10.13053/CyS-17-4-2013-014

À Martín, A Vellido. Cartogram-based data visualization using the Growing Hierarchical SOM, In K. Gibert, V. Botti and R. Reig-Bolaño (eds.)  Artificial Intelligence Research and Development. Procs. of the 16th International Conference of the Catalan Association for Artificial Intelligence (CCIA 2013), pp.249-252, IOS Press.

C König, R Cruz-Barbosa, R Alquézar, A Vellido. SVM-Based Classification of Class C GPCRs from Alignment-Free Physicochemical Transformations of Their Sequences. 2nd International Workshop on Pattern Recognition in Proteomics, Structural Biology and Bioinformatics (PR PS BB 2013), 17th International Conference on Image Analysis and Processing (ICIAP) In A. Petrosino, L. Maddalena, P. Pala (Eds.): ICIAP 2013 Workshops, LNCS 8158, pp. 336–343, 2013, Springer.

R Cruz-Barbosa, A Vellido, J Giraldo. Advances in Semi-Supervised Alignment-Free Classification of G-Protein-Coupled Receptors, In Procs. of the International Work-Conference on Bioinformatics and Biomedical Engineering (IWBBIO'13), Granada, Spain, pp.759-766.

V Ribas Ripoll, E Romero, JC Ruiz-Rodríguez, A Vellido. A Quotient Basis Kernel for the prediction of mortality in severe sepsis patients. In Proceedings of the 21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2013), Bruges, Belgium, pp.379-384.

A Tosi, A Vellido. Robust cartogram visualization of outliers in manifold learning, In Proceedings of the 21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2013), Bruges, Belgium, pp.555-560.

DL García, À Nebot, A Vellido. Visualizing pay-per-view television customers churn using cartograms and flow maps, In Proceedings of the 21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2013), Bruges, Belgium, pp.567-572.

DL Garcia, A  Vellido, À Nebot.  Telecommunication Customers Churn Monitoring Using Flow Maps and Cartogram Visualization. In GRAPP 2013 / IVAPP 2013 - Proceedings of the International Conference on Computer Graphics Theory and Applications and International Conference on Information Visualization Theory and Applications, pp.451-460, Barcelona, Spain

A Vellido, DL García, À Nebot. Cartogram Visualization for Nonlinear Manifold Learning Models. Data Mining and Knowledge Discovery, 27(1):22-54. doi: 10.1007/s10618-012-0294-6

2012

E Biganzoli, A Vellido, F Ambrogi, R Tagliaferri (Editors) Computational Intelligence Methods for Bioinformatics and Biostatistics, LNBI/LNCS 7548, 2012.

PJG Lisboa, IH Jarman, TA Etchells, SJ Chambers, D Bacciu, J Whittaker, JM Garibaldi, S Ortega-Martorell, A Vellido, IO Ellis. Discovering Hidden Pathways in Bioinformatics, LNCS/LNBI 7548, pp 49-60

VJ Ribas, J Caballero López, A Sáez de Tejada, JC Ruiz-Rodríguez, A Ruiz-Sanmartín, J Rello, A Vellido. On the Use of Graphical Models to Study ICU Outcome Prediction in Septic Patients Treated with Statins, LNCS/LNBI 7548, pp 98-111

MI Cárdenas, A Vellido, I Olier, X Rovira, J Giraldo. Complementing Kernel-Based Visualization of Protein Sequences with Their Phylogenetic Tree, LNCS/LNBI 7548, pp 136-149

S Ortega-Martorell, PJG Lisboa, A Vellido, RV Simões, M Pumarola, M Julià-Sapé, C Arús Convex Non-Negative Matrix Factorization for Brain Tumor Delimitation from MRSI Data, PLoS ONE, 7(10):e47824.

S Ortega-Martorell, PJG Lisboa, A Vellido, RV Simões, M Pumarola, M Julià-Sapé, C Arús. Unsupervised tumour area delimitation in glioblastoma multiforme using non-negative matrix factorisation of MRSI grids. European Society for Magnetic resonance in Medicine and Biology Congress (ESMRMB 2012). Accepted for oral presentation.

S Ortega-Martorell, PJG Lisboa, A Vellido, M Julià-Sapé, C Arús. Non-negative Matrix Factorisation methods for the spectral decomposition of MRS data from human brain tumours. BMC Bioinformatics, 13:38. (draft)

A Vellido, E Romero, M Julià-Sapé, C Majós, À Moreno-Torres, and C Arús. Robust discrimination of glioblastomas from metastatic brain tumors on the basis of single-voxel proton MRS. NMR in Biomedicine. 25(6):819–828. (draft)

VJ Ribas, A Vellido, JC Ruiz-Rodríguez, J Rello. Severe sepsis mortality prediction with logistic regression over latent factors. Expert Systems with Applications, 39(2), 1937-1943. (draft)

C Arizmendi, A Vellido, E Romero. Classification of human brain tumours from MRS data using discrete wavelet transform and Bayesian neural networks. Expert Systems with Applications, 39(5), 5223-5232.

C Arizmendi, A Vellido,  E Romero. Preprocessing MRS Information for Classification of Human Brain Tumours. In: R. Magdalena-Benedito, E. Soria, J. Guerrero Martínez, J. Gómez-Sanchis and A.J. Serrano-López (eds.) Medical Applications of Intelligent Data Analysis: Research Advancements, IGI Global, 2012, pp.29-49, doi: 10.4018/978-1-4666-1803-9.ch003

MI Cárdenas, A Vellido, I Olier, X Rovira, J Giraldo. Kernel Generative Topographic Mapping of Protein Sequences. In: R. Magdalena-Benedito, E. Soria, J. Guerrero Martínez, J. Gómez-Sanchis and A.J. Serrano-López (eds.) Medical Applications of Intelligent Data Analysis: Research Advancements, IGI Global, 2012, pp.194-207, doi: 10.4018/978-1-4666-1803-9.ch013

VJ Ribas, JC Ruiz-Rodríguez and A Vellido, Intelligent management of sepsis in the intensive care unit, In: R. Magdalena-Benedito, E. Soria, J. Guerrero Martínez, J. Gómez-Sanchis and A.J. Serrano-López (eds.) Medical Applications of Intelligent Data Analysis: Research Advancements, IGI Global, 2012, pp.1-16, doi:10.4018/978-1-4666-1803-9.ch001

A Tosi, A Vellido.  Cartogram representation of the batch-SOM magnification factor. In Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2012), Bruges, Belgium, pp.203-208.

A Vellido, JD Martín-Guerrero, PJG Lisboa. Making machine learning models interpretable. In Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning ( ESANN 2012), Bruges, Belgium, pp.163-172.

S Ortega-Martorell, PJG Lisboa, A Vellido, RV Simões, M Pumarola, M Julià-Sapé, C Arús, C. Delimitation of the solid tumour area in  glioblastomas using Non-Negative Matrix Factorization, In Procs. of the IEEE 9th International Symposium on Biomedical Imaging (ISBI 2012), abstract.

A Vilamala, LA Belanche and A Vellido. Classifying malignant brain tumours from 1H-MRS data using Breadth Ensemble Learning. In Procs. of the IEEE World Congress on Computational Intelligence (WCCI 2012) International Joint Conference on Artificial Neural Networks (IJCNN 2012), Brisbane, Australia, pp.2803-2810.

H Ruiz, S Ortega-Martorell, IH Jarman, A Vellido, E Romero, JD Martín and PJG Lisboa. Towards Interpretable Classifiers with Blind Signal Separation. In Procs. of the IEEE World Congress on Computational Intelligence (WCCI 2012) International Joint Conference on Artificial Neural Networks (IJCNN 2012), Brisbane, Australia, 3008-3016.
 

2011

Ortega-Martorell, S., Lisboa, P.J.G., Vellido, A., Simões, R.V., Julià-Sapé, M., and Arús, C. Brain tumor pathological area delimitation through Non-negative Matrix Factorization, BioDM' Workshop, The IEEE 11th International Conference on Data Mining Workshops (ICDMW'11) pp.1058-1063

Cruz-Barbosa, R., Bautista-Villavicencio, D., Vellido, A. On the computation of the Geodesic Distance with an application to dimensionality reduction in a neuro-oncology problem. In Procs. of The 16^th Iberoamerican Congress on Pattern Recognition (CIARP 2011), LNCS 7042, pp.483-490.

Ribas, V., Ruiz-Rodríguez, J.D., Wojdel, A., Caballero-López, J., Ruiz-Sanmartín A., Rello, J. and Vellido, A.  Severe sepsis mortality prediction with Relevance Vector Machines. In Procs. of the 33^rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2011), pp.100-103.

Arizmendi, C., Sierra, D.A., Vellido, A., Romero, E. Brain Tumour Classification Using Gaussian Decomposition and Neural Networks. In Procs. of the 33^rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2011), pp.5645-5648.

Ortega-Martorell, S., Olier, I., Vellido, A., Lisboa, P.J.G., El-Deredy, W. Comparing independent component analysis and non-negative matrix factorisation in the identification of event-related brain dynamics,  11th International Conference on Cognitive Neuroscience (ICON XI), Abstract A066, p.82.

Ortega-Martorell, S., Vellido, A., Lisboa, P.J.G., Julià-Sapé, M., and Arús, C. Spectral decomposition methods for the analysis of MRS information from human brain tumours, In Procs. of the 2011 International Joint Conference on Neural Networks (IJCNN 2011), pp.3285-3292.

Cárdenas, M.I., Vellido, A., Olier, I., Rovira, X., Giraldo, J. Visualization of the phylogenetic structure of G Protein-Coupled Receptor sequences using kernel and tree methods, Eigth International Meeting on Computational Intelligence Methods in Bioinformatics and Biostatistics, (CIBB 2011)

Ribas, V.J., Caballero-López, J., Saez de Tejada, A., Ruiz-Rodríguez, J.C., Ruiz-Sanmartín, A., Rello, J., Vellido, A. Bayesian networks for ICU outcome prediction in sepsis patients treated with statin drugs, Eigth International Meeting on Computational Intelligence Methods in Bioinformatics and Biostatistics, (CIBB 2011).

Lisboa, P.J.G., Jarman, I.H., Etchells, T.A. , Chambers, S.J., Bacciu, D., Whittaker, J., Garibaldi, J. M., Ortega-Martorell, S., Vellido, A., and Ellis, I.H. Discovering hidden pathways in bioinformatics, Eigth International Meeting on Computational Intelligence Methods in Bioinformatics and Biostatistics, (CIBB 2011).

Arizmendi, C., Vellido, A., Romero, E., Binary Classification of Brain Tumours Using a Discrete Wavelet Transform and Energy Criteria,  In Procs. of the 2nd IEEE Latin American Symposium on Circuits and Systems (LASCAS 2011), pp.1-4.

Vellido, A.,  Cárdenas, M.I., Olier, I., Rovira, X., and Giraldo, J.  A probabilistic approach to the visual exploration of G Protein-Coupled Receptor sequences, In Procs. of the 19th European Symposiun on Artificial Neural Networks (ESANN 2011), pp.233-238.

Vellido, A., Martín, J.D., Rossi, F., and Lisboa, P.J.G. Seeing is believing: The importance of visualization in real-world machine learning applications, In Procs. of the 19th European Symposiun on Artificial Neural Networks (ESANN 2011), pp.219-226.

Cruz-Barbosa, R., Bautista-Villavicencio, D., and Vellido, A. Comparative diagnostic accuracy of linear and nonlinear feature extraction methods in a neuro-oncology problem. In Procs. of the 3rd Mexican Conference on Pattern Recognition (MCPR 2011) LNCS, Vol.6718, 2011, pp.34-41.

Ribas, V., Caballero-López, J., Ruiz-Rodríguez, J.C., Ruiz Sanmartín, A., Rello, J., and Vellido, A. On the use of decision trees for ICU outcome prediction in sepsis patients treated with statins. In Procs. of the IEEE Symposium Series on Computational Intelligence / IEEE Symposium on Computational Intelligence and Data Mining (IEEE SSCI CIDM 2011), pp.37-43.

Olier, I., Amengual, J. and Vellido, A. A variational Bayesian approach for the robust estimation of cortical silent periods from EMG time series of brain stroke patients. Neurocomputing, 74(9): 1301-1314.

Cruz, R., Vellido, A.,  Semi-supervised analysis of human brain tumours from partially labeled MRS information, using manifold learning models. International Journal of Neural Systems. 21(1): 17-29.
 

2010

Vellido, A. Should you trust what you see? Inroads into data visualization using generative topographic mapping. 3^rd International Conference of the ERCIM WG on Computing & Statistics (ERCIM 2010), London, U.K.

Arizmendi, C., Hernández-Tamames, J., Romero, E., Vellido, A., del Pozo, F., Diagnosis of Brain Tumours from Magnetic Resonance Spectroscopy using Wavelets and Neural Networks. In Procs. of the 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2010) Buenos Aires, Argentina, pp.6074-6077

Colas, F., Kok, J.N., and Vellido, A.  Finding Discriminative Subtypes of Aggressive Brain Tumours using Magnetic Resonance Spectroscopy. In Procs. of the 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2010) Buenos Aires, Argentina

Vellido, A., Castro, F., Nebot, A. Clustering Educational Data. In Romero, C., Ventura, S., Pechenizkiy, M., Baker, R.S.J.d. (eds.) Handbook of Educational data Mining, CRC Press, Taylor & Francis Group, pp.75-92.

Lisboa, P.J.G.  Vellido, A.  Tagliaferri, R.  Napolitano, F.  Ceccarelli, M.  Martin-Guerrero, J.D.  Biganzoli, E. Data Mining in Cancer Research, IEEE Computational Intelligence Magazine, 5(1), 14-18

Ortega-Martorell, S., Olier, I., Vellido, A., Julià-Sapé, M., Arús, C. Spectral Prototype Extraction for the discrimination of glioblastomas from metastases in a SV ¹H-MRS brain tumour database. ISMRM-ESMRMB Joint Annual Meeting.

Lisboa, P.J.G., Vellido, A., Martín, J.D. Computational Intelligence in biomedicine: Some contributions. In Procs. of the 18th European Symposiun on Artificial Neural Networks (ESANN 2010), 429-438.

Olier, I., Amengual, J., Vellido, A. Segmentation of EMG time series using a variational Bayesian approach for the robust estimation of cortical silent periods. In Procs. of the 18th European Symposiun on Artificial Neural Networks (ESANN 2010), 439-444.

Olier, I., Vellido, A., Giraldo, J. Kernel Generative Topographic Mapping. In Procs. of the 18th European Symposiun on Artificial Neural Networks (ESANN 2010), 481-486.

Ortega-Martorell, S., Olier, I., Vellido, A., Julià-Sapé, M., Arús, C. Spectral Prototype Extraction for dimensionality reduction in brain tumour diagnosis. In Procs. of the 18th European Symposiun on Artificial Neural Networks (ESANN 2010), 445-450.

Cruz, R., Vellido, A. Semi-Supervised Geodesic Generative Topographic Mapping. Pattern Recognition Letters, 31(3), 202-209.

González-Navarro, F.F., Belanche-Muñoz, Ll.A., Romero, E., Vellido, A., Julià-Sapé, M., Arús, C. Feature and model selection with discriminatory visualization for diagnostic classification of brain tumours. Neurocomputing, 73(4-6), 622-632.

2009

Cruz, R., and Vellido, A., Semi-supervised Outcome Prediction for a Type of Human Brain Tumour Using Partially Labeled MRS Information. In Procs. of the Intelligent Data Engineering and Automated Learning  (IDEAL 2009) International Conference, Lecture Notes in Computer Science, LNCS 5788, 168-175

Olier, I., Vellido, A. Clustering and Visualization of Multivariate Time Series. In Soria-Olivas, E. et al. (eds.) The Handbook of Research on Machine Learning Applications and Trends: Algorithms, Methods, and Techniques, Vol.I, ch.8, pp.176-194. Information Science Reference, New York.

Vellido, A., Romero, E., González-Navarro, F.F., Belanche-Muñoz, Ll., Julià-Sapé, M., Arús, C. Outlier exploration and diagnostic classification of a multi-centre ¹H-MRS brain tumour database. Neurocomputing, 72(13-15), 3085-3097.

Romero, E., Vellido, A., Julià-Sapé, M, and Arús, C. Discriminating glioblastomas from metastases in a SV 1H-MRS brain tumour database. In Proceedings of the 26th Annual Meeting of the European Society for Magnetic Resonance in Medicine and Biology (ESMRMB 2009), Antalya, Turkey, p.18.

Arizmendi, A., Vellido, A., Romero, E. Frequency Selection for the Diagnostic Characterization of Human Brain Tumours. In Procs. of the CCIA 2009.

Cruz, R., and Vellido, A. Comparative evaluation of Semi-Supervised Geodesic GTM. In The 4^th International Conference on Hybrid Artificial Intelligence Systems (HAIS 2009). LNAI 5573, pp.344-351.

Romero, E., Vellido, A., and Sopena, J.M. Feature selection with single-layer perceptrons for a multicentre ¹H-MRS brain tumour database. In The 10^th International Work-Conference on Artificial Neural Networks (IWANN 2009). LNCS 5517, pp.1013–1020.

Vellido, A., and Lisboa, P.J.G. (eds.) Investigating Human Cancer with Computational Intelligence Techniques. In KES Recent Research Results series. Future Technology Press. ISBN: 978-0-9561516-0-5

Vellido, A. and Lisboa, P.J.G. Preface to Investigating Human Cancer with Computational Intelligence Techniques. In KES Recent Research Results series. Future Technology Press, vii-viii.

Nebot, A., Castro, F., Vellido, A., Julià-Sapé, M., and Arús, C. Rule-based assistance to brain tumour diagnosis using LR-FIR. In Investigating Human Cancer with Computational Intelligence Techniques. KES Recent Research Results series. Future Technology Press, 83-92.

Vellido, A., Julià-Sapé, M., Romero, E., and Arús, C. Exploratory characterization of a multi-centre ¹H-MRS brain tumour database. In Investigating Human Cancer with Computational Intelligence Techniques. KES Recent Research Results series. Future Technology Press, 55-67.

Schleif, F.-M., Biehl, M., and Vellido, A. Advances in machine learning and computational intelligence. Neurocomputing, 72(7-9), 1377-1378.

2008

Olier, I., Vellido, A. Variational Bayesian Generative Topographic Mapping. Journal of Mathematical Modelling and Algorithms, 7(4), 371-387.

Lisboa, P.J.G., Romero, E., Vellido, A., Julià-Sapé, M., and Arús, C. Classification, Dimensionality Reduction, and Maximally Discriminatory Visualization of a Multicentre ¹H-MRS Database of Brain Tumors. In The Seventh International Conference on Machine Learning and Applications (ICMLA'08), 613-618.

Cruz, R., Vellido, A. On the Improvement of the Mapping Trustworthiness and Continuity of a Manifold Learning Model. In Procs. of the 9th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL 2008). LNCS 5326, 266-273.

Vellido, A., Julià-Sapé, M., Romero, E., and Arús, C. Exploring outlierness and its causes in a ¹H-MRS brain tumour database. In Proceedings of e-TUMOUR Workshop ‘Towards Brain Tumour Classification by Molecular Profiling’, Valencia, Spain.

Vellido, A., Julià-Sapé, M., Romero, E., and Arús, C. Nonlinear dimensionality reduction for the exploration of outliers in a multicentre ¹H-MRS database of brain tumours. In Proceedings of the 25th Annual Meeting of the European Society for Magnetic Resonance in Medicine and Biology (ESMRMB).

Cruz, R., Vellido, A. Geodesic Generative Topographic Mapping. In Proceedings of the 11th Ibero-American Conference on Artificial Intelligence (IBERAMIA 2008). LNAI 5290, 113-122.

Cruz, R., Vellido, A. Unfolding the Manifold in Generative Topographic Mapping. In Proceedings of the 3rd International Workshop on Hybrid Artificial Intelligence Systems (HAIS 2008). LNAI 5271, 392-399 .

Olier, I., Vellido, A. Advances in Clustering and Visualization of Time Series using GTM Through Time. Neural Networks, 21(7), 904-913.  preprint

Castro, F., Vellido, A.,  Nebot, À., and Minguillón, J. Detección de Estudiantes con Comportamiento Atípico en Entornos de Aprendizaje e-Learning. In Llamas-Nistal, M., Vaz de Carvalho, C., and Rueda Artunduaga, C. (eds.) TICAI2006: TICs para el Aprendizaje de la Ingeniería, pp.23-30. IEEE, Sociedad de Educación: Capítulos Español, Portugués y Colombiano.

Nebot, A., Castro, F., Vellido, A., Julià-Sapé, M. and Arús, C. Rule-based assistance to brain tumour diagnosis using LR-FIR. In Procs. of the 12th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES2008). LNAI 5178, Vol. II, 173-180.

Vellido, A., Julià-Sapé, M., Romero, E. and Arús, C. Exploratory characterization of outliers in a multi-centre ¹H-MRS brain tumour dataset. In Procs. of the 12th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES2008). LNAI 5178, Vol. II, 189-196.

Vellido, A., Velazco, J.S. Assessment of the effect of noise on an unsupervised feature selection method for Generative Topographic Mapping. In 10th International Conference on Enterprise Information Systems (ICEIS 2008).

Vellido, A., Velazco, J.S. The effect of noise and sample size on an unsupervised feature selection method for manifold learning. In Procs. of the International Joint Conference on Neural Networks (IJCNN 2008), 523-528.

Olier, I., Vellido, A. On the benefits for model regularization of a Variational formulation of GTM. In Procs. of the International Joint Conference on Neural Networks (IJCNN 2008), 1569-1576.

Olier, I., Vellido, A. A Variational Formulation for GTM Through Time. In Procs. of the International Joint Conference on Neural Networks (IJCNN 2008), 517-522.

Vellido, A., Biganzoli, E., Lisboa, P.J.G. Machine learning in cancer research: implications for personalised medicine. In Procs. of the 16th European Symposiun on Artificial Neural Networks (ESANN 2008), 55-64.

Romero, E., Julià-Sapé, M., Vellido, A. DSS-oriented exploration of a multi-centre magnetic resonance spectroscopy brain tumour dataset through visualization. In Procs. of the 16th European Symposiun on Artificial Neural Networks (ESANN 2008), 95-100.

Cruz, R., Vellido, A. Two-Stage Clustering of a Human Brain Tumour Dataset Using Manifold Learning Models. In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing, BIOSIGNALS 2008, Funchal, Madeira, Portugal. INSTICC Press. pp.191-196.

Vellido, A., Lisboa, P.J.G. Machine Learning in Human Cancer Research. In Svensson, H.A. (Ed.): Neurocomputing Research Developments. Nova Publishers, 163-180.

2007

Olier, I., Vellido, A. Variational GTM. In 8th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL 2007), LNCS Vol.4881, 77-86. (pdf)

Cruz, R., Vellido, A. Mining a Human Brain Tumour Dataset for Clinical Decision Support : Advances in the AIDTumour Project. In MICAI 2007, Workshop on Data Mining Applications & Current Trends.

Olier, I., Vellido, A. A Variational Bayesian Formulation for GTM Through Time: Theoretical Foundations. Technical Report LSI-07-38-R.Universitat Politècnica de Catalunya, UPC, Barcelona, Spain. (download)

Olier, I., Vellido, A. A Variational Bayesian Formulation for GTM: Theoretical Foundations. Technical Report LSI-07-33-R.Universitat Politècnica de Catalunya, UPC, Barcelona, Spain. (download)

Cruz, R., Vellido, A. On the Influence of Class Information in the Two-Stage Clustering of a Human Brain Tumour Dataset. In Procs. of the 6th Mexican Conference on Artificial Intelligence (MICAI 2007). LNAI 4827, 472-482. (pdf)

Cruz, R., Vellido, A. On the Initialization of Two-Stage Clustering with class-GTM. In Proceedings of the 12th Conference of the Spanish Association for Artificial Intelligence, CAEPIA+TTIA 2007, LNAI Vol.4788, 50-59. (pdf)

Cruz, R., Vellido, A. Evaluation of a Two-Stage Clustering Procedure Using Class Information in Generative Topographic Mapping. In I.Rojas Ruiz and H. Pomares Cintas (eds.) Actas del II Simposio de Inteligencia Computacional (IEEE SICO 2007), Thomson, pp.17-24.

García, D.L. , Vellido, A. and Nebot, A., Finding relevant features for the churn analysis-oriented segmentation of a telecommunications market. In I.Rojas Ruiz and H. Pomares Cintas (eds.) Actas del II Simposio de Inteligencia Computacional (IEEE SICO 2007), Thomson, pp.301-310.

Vellido, A., Andrade, A.O. Determination of feature relevance for the grouping of motor unit action potentials through a generative mixture model. Biomedical Signal Processing and Control, 2(2), 111-121.

Cruz, R., Vellido, A. Limits to the Use of Class Information in a GTM-based Two-Stage Clustering Procedure. In F.J.Ferrer-Troyano et al. (eds.), Actas del IV Taller Nacional de Minería de Datos y Aprendizaje (TAMIDA 2007), Thomson, pp.303-312.

Castro, F., Vellido, A., Nebot, A., Mugica, F. Applying Data Mining Techniques to e-Learning Problems. In: Jain, L.C., Tedman, R.A. and Tedman, D.K. (eds.) Evolution of Teaching and Learning Paradigms in Intelligent Environment. Studies in Computational Intelligence, Vol.62, Springer-Verlag, 183-221.

Vellido, A., Castro, F., Etchells, T.A., Nebot, A., and Mugica, F. Data Mining of Virtual Campus Data. In: Jain, L.C., Tedman, R.A and Tedman, D.K (eds.) Evolution of Teaching and Learning Paradigms in Intelligent Environment. Studies in Computational Intelligence, Vol.62, Springer-Verlag, 223-254.

Vellido, A., Lisboa, P.J.G. Neural networks and other machine learning methods in cancer research. In Procs. of IWANN 2007.LNCS Vol. 4507, 964-971. (pdf)

García, D.L. , Vellido, A. and Nebot, A., Identification of churn routes in the Brazilian telecommunications market. In Procs. of the 15th European Symposiun on Artificial Neural Networks, ESANN 2007, 585-590.

Vellido, A., Martí, E., Comas, J., Rodríguez-Roda, I., and Sabater, F. Exploring the ecological status of human altered streams through Generative Topographic Mapping. Environmental Modelling & Software, 22(7), 1053-1065. (download).

García, D.L. , Vellido, A. and Nebot, A., Predictive Models in Churn Data Mining: A Review. Technical Report LSI-07-4-R, Universitat Politècnica de Catalunya, UPC, Barcelona, Spain. (download)

García, D.L, Vellido, A., and Nebot, A. Customer Continuity Management as a Foundation for Churn Data Mining. Technical Report LSI-07-2-R. Universitat Politècnica de Catalunya, UPC, Barcelona, Spain. (download)

Cruz, R. and Vellido, A., Elements of Generative Manifold Learning for semi-supervised tasks. Technical Report LSI-07-1-R. Universitat Politècnica de Catalunya, UPC, Barcelona, Spain. (download)

2006

Vellido, A., Missing data imputation through GTM as a mixture of t-distributions. Neural Networks, 19(10), 1624-1635. (download)

Cruz, R. and Vellido, A., Clustering of brain tumours through constrained manifold learning using class information. Learning'06 International Conference. 'Between Learning and Data Mining' special session, 3rd of October, Vilanova i la Geltrú, Spain.

Spate, J., Gibert, K., Sànchez-Marrè, M., Frank, E., Comas, J., Athanasiadis, I., and Vellido, A. Data Mining as a Tool for Analysing Environmental Systems. Learning'06 International Conference. 'Between Learning and Data Mining' special session, 3rd of October, Vilanova i la Geltrú, Spain.

Poveda, J. and Vellido, A. Neural Network Models for Language Acquisition: A Brief Survey. In Proc. of the 7th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL 2006), Burgos, Spain. LNCS Vol.4224, 1346-1357.

Olier, I. and Vellido, A., Time Series Relevance Determination through a topology-constrained Hidden Markov Model. In Proc. of the 7th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL 2006), Burgos, Spain. LNCS Vol.4224, 40-47. (download)

Vellido, A. , Etchells, T.A. , García, D.L. and Nebot, À., Describing customer loyalty to Spanish petrol stations through rule extraction. In Proc. of the 7th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL 2006), Burgos, Spain. LNCS Vol.4224, 970-977.

Vellido, A., Assessment of an Unsupervised Feature Selection Method for Generative Topographic Mapping. 16th International Conference on Artificial Neural Networks (ICANN 2006), Athens, Greece. LNCS Vol.4132, 361-370. (download)

Cruz, R. and Vellido, A., On the improvement of brain tumour data clustering using class information, In Proc. of the 3rd European Starting AI Researcher Symposium (STAIRS'06), Riva del Garda, Italy.

Olier, I. and Vellido, A., Capturing the Dynamics of Multivariate Time Series Through Visualization Using Generative Topographic Mapping Through Time, In proceedings of the 1st IEEE International Conference on Engineering of Intelligent Systems, IEEE ICEIS'2006, Islamabad, Pakistán, pp.492-497. (download)

Etchells,T.A., Nebot, A., Vellido, A., Lisboa, P.J.G., and Múgica, F., Learning what is important: feature selection and rule extraction in a virtual course. In Proceedings of the 14th European Symposium on Artificial Neural Networks, ESANN 2006, Bruges, Belgium, 401-406.

Etchells, T.A., Vellido, A., Martí, E., Lisboa, P.J.G., and Comas, J., On the prediction of the ecological status of human-altered streams and its rule-based interpretation. 3rd Biennial meeting of the International Environmental Modelling and Software Society, iEMSs'2006. Data Mining Workshop.

Vellido, A., Comas, J., Cruz, R., and Martí, E., Finding relevant features for the characterization of the ecological status of human altered streams using a constrained mixture model. 3rd Biennial meeting of the International Environmental Modelling and Software Society, iEMSs'2006. Data Mining Workshop.

Vellido, A., Lisboa, P.J.G., and Vicente, D. Robust analysis of MRS brain tumour data using t-GTM. Neurocomputing, 69(7-9), 754-768. (pdf)

Andrade, A., and Vellido, A., (2006) Determining feature relevance for the grouping of motor unit action potentials through generative topographic mapping, In Proc. of the 25th IASTED International ConferenceModelling, Identification, and Control (MIC'06), Feb.6-8, Lanzarote, Canary Islands, Spain, pp.507-512. (pdf)

Vellido, A. and Lisboa, P.J.G., Handling outliers in brain tumour MRS data analysis through robust topographic mapping, Computers in Biology and Medicine, 36(10), 1049-1063. (download) 

Vellido, A., Castro, F., Nebot, A., and Múgica, F., (2006) Characterization of atypical virtual campus usage behavior through robust generative relevance analysis, In Proc. of the Fifth IASTED International Conference on Web-Based Education (WBE 2006), Jan.23-25, Puerto Vallarta, México, pp.183-188.

Nebot, A., Castro, F., Múgica, F., and Vellido, A., (2006) Identification of fuzzy models to predict students performance in an e-learning environment, In Proc. of the Fifth IASTED International Conference on Web-Based Education (WBE 2006), Jan.23-25, Puerto Vallarta, México, pp.74-79. [Shortlisted for The Fifth International Competition of Ph.D. Students in the WBE Area]

2005

Olier, I. and Vellido, A. , (2005) Capturing the dynamics of multivariate time series through visualization using Generative Topographic Mapping Through Time. Technical Report LSI-05-52-R, Universitat Politècnica de Catalunya (UPC), Barcelona, Spain.

Castro, F. , Vellido, A. , Nebot, A. and Minguillón, J. (2005) Detecting atypical student behaviour on an e-learning system. VI Congreso Nacional de Informática Educativa; I Simposio Nacional de Tecnologías de la Información y las Comunicaciones en la Educación, SINTICE’2005, Granada, Spain, September 2005, 153-160.

Castro, F. , Vellido, A. , Nebot, A. and Minguillón, J. (2005) Finding relevant features to characterize student behavior on an e-learning system. In: International Conference on Frontiers in Education: Computer Science and Computer Engineering (FECS 2005), Las Vegas, Nevada, USA, June 2005.

Olier, I. and Vellido, A. , (2005) Comparative assessment of the robustness of missing data imputation through Generative Topographic Mapping, In  Cabestany, J., Prieto, A., and Sandoval, F. (Eds.) Proc. of the IWANN 2005, Vilanova i la Geltru, Barcelona, Spain. LNCS Vol.3512, 771-778.

Vellido, A. (2005) Preliminary theoretical results on a feature relevance determination method for Generative Topographic Mapping. Technical Report LSI-05-13-R, Universitat Politècnica de Catalunya, UPC, Barcelona, Spain.

Vellido, A., Lisboa, P.J.G., and Vicente,D. (2005) Handling outliers and missing data in brain tumor clinical assessment usign t-GTM. In Proc. of the European Symposium on Artificial Neural Networks, ESANN 2005, Bruges, Belgium, 121-126.

Vellido, A. and Lisboa, P.J.G. (2005) Functional topographic mapping for robust handling of outliers in brain tumour data. In Proc. of the European Symposium on Artificial Neural Networks, ESANN 2005, Bruges, Belgium, 133-138.

2004

Olier, I., and Vellido, A. (2004) Assessing the robustness of missing data imputation through Generative Topographic Mapping. ACIA Newsletter, 31, 14-20.

Vicente, D. and Vellido, A. (2004) Review of Hierarchical Models for Data Clustering and Visualization. In R.Giráldez, J.C. Riquelme, J.S. Aguilar-Ruiz (Eds.) Tendencias de la Minería de Datos en España. Red Española de Minería de Datos.

Alquézar, R., Belanche, L., Nebot, A., Romero, E., and Vellido, A. (2004) Investigación actual del grupo SOCO: Metodologías híbridas de Soft Computing. In R.Giráldez, J.C. Riquelme, J.S. Aguilar-Ruiz (Eds.) Tendencias de la Minería de Datos en España. Red Española de Minería de Datos.

Vellido, A. (2004) Missing data imputation through Generative Topographic Mapping as a mixture of t-distributions: Theoretical developments. Technical Report LSI-04-50-R, Universitat Politècnica de Catalunya (UPC), Barcelona, Spain.

Vellido, A. (2004) Generative Topographic Mapping as a constrained mixture of Student t-distributions: Theoretical developments. Technical Report LSI-04-44-R, Universitat Politècnica de Catalunya (UPC), Barcelona, Spain.

Vicente, D., Vellido, A., Martí, E., Comas, J., and Rodriguez-Roda, I. (2004), “Exploration of the ecological status of Mediterranean rivers: Clustering, visualizing and reconstructing streams data using Generative Topographic Mapping”. In W.I.T. Transactions on Information and Communication Technologies, Vol.33, 121-130.

Vellido, A., Olier, I., Martí, E., Comas, J., and Rodríguez-Roda, I. (2004) STREAMES project: Exploration of the ecological status of Mediterranean rivers using Generative Topographic Mapping. Poster presentation at BESAI 2004 - 4th ECAI Workshop on Binding Environmental Sciences and Artificial Intelligence. August 2004, Valencia, Spain.

Vellido, A., El-Deredy, W., and Lisboa, P.J.G. (2004) Studying embedded human EEG dynamics using Generative Topographic Mapping . Technical Report LSI-04-8-R, Universitat Politècnica de Catalunya (UPC), Barcelona, Spain.

Vellido, A., and El-Deredy, W. (2004) Exploring dopamine-mediated reward processing through the analysis of EEG-measured gamma-band brain oscillations. Technical Report LSI-04-7-R, Universitat Politècnica de Catalunya (UPC), Barcelona, Spain.

2003 

Vellido, A., El-Deredy, W., Gruber, T., Zald, D.H., McGlone, F.P., and Müller, M.M. (2003) Reward predictability and oscillatory brain processes. In The Annual Computational Neuroscience Meeting (CNS'2003). Poster presentation. Alicante, Spain.

Vellido, A., El-Deredy, W., and Lisboa, P.J.G. (2003) Selective Smoothing of the Generative Topographic Mapping. IEEE Transactions on Neural Networks, 14(4), 847-852.

2002

Vellido, A., Lisboa, P.J.G. and Meehan, K. (2002) Characterizing and Segmenting the On-Line Customer Market Using Neural Networks. In E-Commerce and Intelligent Methods. Heidelberg: Springer-Verlag, 101-119.

Vellido, A. (2002) Neural networks for B2C e-commerce analysis: some elements of best practice. In Proceedings of the 4th International Conference On Enterprise Information Systems (ICEIS 2002) Ciudad Real, Spain.

2001

Vellido, A and Lisboa, P.J.G (2001) An electronic commerce application of the Bayesian Framework for MLPs: the Effect of Marginalization and ARD. Neural Computing & Applications. 10(1), 3-11.

2000

Lisboa, P.J.G., Vellido, A. and Edisbury, B. (Editors, 2000) Business Applications of Neural Networks. Singapore: World Scientific.

Lisboa, P.J.G., Vellido, A. (2000) Preface: Business Applications of Neural Networks. In Business Applications of Neural Networks. Singapore: World Scientific.

Vellido, A., Lisboa, P.J.G. and Meehan, K. (2000) Characterizing and segmenting the business-to-consumer e-commerce market using neural networks. In Business Applications of Neural Networks. Singapore: World Scientific.

Lisboa, P.J.G., Vellido, A. and Wong, H. (2000) Outstanding Issues for Clinical Decision Support with Neural Networks. In Artificial Neural Networks in Medicine and Biology. Springer, London, 63-71.

Vellido, A., Lisboa, P.J.G. and Meehan, K. (2000) The Generative Topographic Mapping as a principled model for data visualization and market segmentation: an electronic commerce case study. International Journal of Computers, Systems, and Signals. 1(2), 119-138.

Vellido, A, Lisboa, P.J.G and Meehan, K. (2000) Quantitative characterization and prediction of on-line purchasing behaviour: a latent variable approach. International Journal of Electronic Commerce, 4(4), 83-104.

Lisboa. P.J.G., Vellido, A. and Wong, H. (2000) Bias reduction in skewed binary classification with Bayesian neural networks. Neural Networks. 13, 407-410.

Vellido, A., Lisboa, P.J.G. & Meehan, K. (2000) Segmenting the e-commerce market using the Generative Topographic Mapping. In Proceedings of the MICAI-2000, 470-481. Acapulco, México.

Vellido, A., Lisboa, P.J.G. & Meehan, K. (2000) A systematic quantitative methodology for characterizing the business-to-consumer e-commerce market. ACM SIGBIO Newsletter,  Vol.20(1), p.24.

1999

Vellido, A., Lisboa, P.J.G and Vaughan, J. (1999) Neural networks in business: a survey of applications (1992-1998) Expert Systems with Applications. 17, 51-70.

Vellido, A., Lisboa, P.J.G and Meehan, K. (1999) Segmentation of the on-line shopping market using neural networks. Expert Systems with Applications. 17, 303-314.

1998

Lisboa, P.J.G., Kirby, S.J.P., Vellido, A., Lee, Y.Y.B. and El-Deredy, W. (1998) Assessment of Statistical and Neural Network Methods in NMR Spectral Classification and Metabolite Selection. Nuclear Magnetic Resonance in Biomedicine. 11, 225-234.

Lisboa, P.J.G., Wong, H., Vellido, A., Kirby, S.P.J., Harris, P. and Swindell, R. (1998) Survival of Breast Cancer Patients Following Surgery: a Detailed Assessment of the Multi-Layer Perceptron and Cox’s Proportional Hazard Model. In Proceedings of the World Congress on Computational Intelligence, IJCNN'1998, 112-116. Anchorage, Alaska, USA.

Lisboa, P.J.G., Vellido, A., Aung, H., El-Deredy, W., Lee, Y.Y.B. and Kirby, S.P.J. (1998) Quantification of uncertainty in tissue characterisation with NMR Spectra. In Proceedings of the 6th Scientific Meeting of the International Society for Magnetic Resonance in Medicine, Abstract 1851, Sidney, Australia

Y. Lee,A. Vellido,W. El-Deredy,S. Kirby,P. Lisboa (1998) Neural networks in practice-an example from magnetic resonance spectroscopy. In Proc. of the IEE Colloquium on Intelligent Methods in Healthcare and Medical Applications (Digest No. 1998/514)

1997

P. Lisboa,S. Kirby,A. Vellido,B. Lee (1997) Pattern recognition methods for MRS analysis and classification. In Proc. of the IEE Colloquium on Realising the Clinical Potential of Magnetic Resonance Spectroscopy: The Role of Pattern Recognition (Digest No: 1997/082)

Lisboa, P.J.G., El-Deredy, W., Vellido, A., Etchells, T. and Pountney, D.C. (1997) Automatic Variable Selection and Rule Extraction Using Neural Networks. In Proceedings of the 15th IMACS World Congress on Scientific Computation, Modelling and Applied Mathematics, 461-466, Berlin, Germany

Lisboa, P.J.G, Branston, N.M., El-Deredy, W. and Vellido, A. (1997) Tissue Characterisation with NMR Spectroscopy; Current State and Future Prospects for the Application of Neural Networks Analysis. In Proceedings of the IEEE/INNS International Joint Conference on Neural Networks, IJCNN'1997, 1385-1390, Houston, USA, pp.1385-1390.

Lisboa, P.J.G., Vellido, A., El-Deredy, W. and Auer, D. (1997) Pattern Recognition Analysis of MRS:A Benchmark of Linear Statistical Methods and Neural Networks. In Proceedings of the 14th Annual Meeting of the European Society of Magnetic Resonance in Medicine and Biology, Abstract 224, Brussels, Belgium.

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By type

Books and chapters in books 

M Hueso, A Valencia, J Carbonell, R Álvarez and A Vellido (2023) Complex Data Representations, Modeling and Computational Power for a Personalized Dialysis. In C.P. Sharma, T. Chandy, V. Thomas (eds.) Artificial Intelligence in Tissue and Organ Regeneration. Academic Press, pp.219-236

A Vellido, V Ribas (2022) AI and ICU Monitoring on the Wake of the COVID-19 Pandemic, In: N Lidströmer, YC Eldar (eds.) Artificial Intelligence in Covid-19, Springer, pp.169-174. https://doi.org/10.1007/978-3-031-08506-2

D. Bacciu, PJG Lisboa, A Vellido (Eds) Deep Learning in Biology and Medicine, World Scientific, 2022.

D. Bacciu, PJG Lisboa, A Vellido. Introduction, in D. Bacciu, PJG Lisboa, A Vellido (Eds) Deep Learning in Biology and Medicine, World Scientific, pp.1-10, 2022.

A. Vellido, P.J.G. Lisboa, J.D. Martín, The Need for Interpretable and Explainable Deep Learning in Medicine and Healthcare. in D. Bacciu, PJG Lisboa, A Vellido (Eds) Deep Lerning in Biology and Medicine, World Scientific, pp.247-264, 2022.

S Behrouzieh, M Keshavarz-Fathi, A Vellido, S Seyedpour, S Adiban, A Vahed, T Dorigo and N Rezaei (2022) Ethical Deliberation on AI-Based Medicine. In: Rezaei, N. (eds) Multidisciplinarity and Interdisciplinarity in Health. Integrated Science, vol 6, pp.567-592. Springer, Cham. https://doi.org/10.1007/978-3-030-96814-4_25

N Rezaei, A Saghazadeh, AR Izaini Ghani, A Vedadhir, A Vahed, A Vellido, et al. (2022). Integrated Science 2050: Multidisciplinarity and Interdisciplinarity in Health. In: Rezaei, N. (eds) Multidisciplinarity and Interdisciplinarity in Health. Integrated Science, vol 6. Springer, Cham, pp.661-690. https://doi.org/10.1007/978-3-030-96814-4_30

A Vellido, V Ribas, Artificial Intelligence in Critical Care: The Path From Promise to Practice. In N Lidströmer and H Ashrafian (eds) Artificial Intelligence in Medicine. Springer Nature, Reference series, ch.106, pp.1469-1478, 2022.

Biganzoli, E., Vellido, A., Ambrogi, F., Tagliaferri, R. (Editors) Computational Intelligence Methods for Bioinformatics and Biostatistics, LNBI/LNCS 7548, 2012.

Ribas, V.J., Ruiz-Rodríguez, J.C., and Vellido, A. (2012) Intelligent management of sepsis in the intensive care unit. In: R. Magdalena-Benedito, E. Soria, J. Guerrero Martínez, J. Gómez-Sanchis and A.J. Serrano-López (eds.) Medical Applications of Intelligent Data Analysis: Research Advancements, IGI Global, 2012, pp.1-16, doi:10.4018/978-1-4666-1803-9.ch001

Arizmendi, C.J., Vellido, A., Romero, (2012) E. Preprocessing MRS Information for Classification of Human Brain Tumours. In: R. Magdalena-Benedito, E. Soria, J. Guerrero Martínez, J. Gómez-Sanchis and A.J. Serrano-López (eds.) Medical Applications of Intelligent Data Analysis: Research Advancements, IGI Global, 2012, pp.29-49, doi: 10.4018/978-1-4666-1803-9.ch003

Cárdenas, M.I., Vellido, A., Olier, I., Rovira, X., Giraldo, J. (2012) Kernel Generative Topographic Mapping of Protein Sequences. In: R. Magdalena-Benedito, E. Soria, J. Guerrero Martínez, J. Gómez-Sanchis and A.J. Serrano-López (eds.) Medical Applications of Intelligent Data Analysis: Research Advancements, IGI Global, 2012, pp.194-207, doi: 10.4018/978-1-4666-1803-9.ch013

Vellido, A., Castro, F., Nebot, A. (2010) Clustering Educational Data. In Romero, C., Ventura, S., Pechenizkiy, M., Baker, R.S.J.d. (eds.) Handbook of Educational data Mining, CRC Press, Taylor & Francis Group, pp.75-92.

Olier, I., Vellido, A. (2009) Clustering and Visualization of Multivariate Time Series. In Soria-Olivas, E. et al. (eds.) The Handbook of Research on Machine Learning Applications and Trends: Algorithms, Methods, and Techniques, Vol.I, ch.8, pp.176-194. Information Science Reference, New York.

Vellido, A., and Lisboa, P.J.G. (eds.) Investigating Human Cancer with Computational Intelligence Techniques. In KES Recent Research Results series. Future Technology Press. 2009. ISBN: 978-0-9561516-0-5

Vellido, A. and Lisboa, P.J.G. (2009) Preface to Investigating Human Cancer with Computational Intelligence Techniques. In KES Recent Research Results series. Future Technology Press, vii-viii.

Nebot, A., Castro, F., Vellido, A., Julià-Sapé, M., and Arús, C. (2009) Rule-based assistance to brain tumour diagnosis using LR-FIR. In Investigating Human Cancer with Computational Intelligence Techniques. KES Recent Research Results series. Future Technology Press, 83-92.

Vellido, A., Julià-Sapé, M., Romero, E., and Arús, C. (2009) Exploratory characterization of a multi-centre ¹H-MRS brain tumour database. In Investigating Human Cancer with Computational Intelligence Techniques. KES Recent Research Results series. Future Technology Press, 55-67.

Castro, F., Vellido, A.,  Nebot, À., and Minguillón, J. (2008) Detección de Estudiantes con Comportamiento Atípico en Entornos de Aprendizaje e-Learning. In Llamas-Nistal, M., Vaz de Carvalho, C., and Rueda Artunduaga, C. (eds.) TICAI2006: TICs para el Aprendizaje de la Ingeniería, pp.23-30. IEEE, Sociedad de Educación: Capítulos Español, Portugués y Colombiano.

Vellido, A., Lisboa, P.J.G. (2008) Machine Learning in Human Cancer Research. In Svensson, H.A. (Ed.): Neurocomputing Research Developments. Nova Publishers, 163-180.

Castro, F., Vellido, A., Nebot, A., Mugica, F. (2007) Applying Data Mining Techniques to e-Learning Problems. In: Jain, L.C., Tedman, R. and Tedman, D. (eds.) Evolution of Teaching and Learning Paradigms in Intelligent Environment. Studies in Computational Intelligence, Vol.62, Springer-Verlag, 183-221.

Vellido, A., Castro, F., Etchells, T.A., Nebot, A., and Mugica, F. (2007) Data Mining of Virtual Campus Data. In: Jain, L.C., Tedman, R. and Tedman, D. (eds.) Evolution of Teaching and Learning Paradigms in Intelligent Environment. Studies in Computational Intelligence, Vol.62, Springer-Verlag, 223-254.

Vicente, D. and Vellido, A. (2004) Review of Hierarchical Models for Data Clustering and Visualization. In R.Giráldez, J.C. Riquelme, J.S. Aguilar-Ruiz (Eds.) Tendencias de la Minería de Datos en España. Red Española de Minería de Datos.

Alquézar, R., Belanche, L., Nebot, A., Romero, E., and Vellido, A. (2004) Investigación actual del grupo SOCO: Metodologías híbridas de Soft Computing. In R.Giráldez, J.C. Riquelme, J.S. Aguilar-Ruiz (Eds.) Tendencias de la Minería de Datos en España. Red Española de Minería de Datos.

Vellido, A., Lisboa, P.J.G. and Meehan, K. (2002) Characterizing and Segmenting the On-Line Customer Market Using Neural Networks. In E-Commerce and Intelligent Methods. Heidelberg: Springer-Verlag, 101-119

Lisboa, P.J.G., Vellido, A. and Edisbury, B. (Editors, 2000) Business Applications of Neural Networks. Singapore: World Scientific.

Lisboa, P.J.G., Vellido, A. (2000) Preface: Business Applications of Neural Networks. In Business Applications of Neural Networks. Singapore: World Scientific.

Vellido, A., Lisboa, P.J.G. and Meehan, K. (2000) Characterizing and segmenting the business-to-consumer e-commerce market using neural networks. In Business Applications of Neural Networks. Singapore: World Scientific.

Lisboa, P.J.G., Vellido, A. and Wong, H. (2000) Outstanding Issues for Clinical Decision Support with Neural Networks. In Artificial Neural Networks in Medicine and Biology. Springer, London, 63-71.

Journals

G Ungan, A Pons-Escoda, D Ulinic, C Arús, S Ortega-Martorell, I Olier, A Vellido, C Majós, M Julià-Sapé (2024) Early pseudoprogression and progression lesions in glioblastoma patients are both metabolically heterogeneous. NMR in Biomedicine, 37(4):e5095 https://doi.org/10.1002/nbm.5095

C Pitarch, G Ungan, M Julià-Sapé, A Vellido (2024) Advances in the Use of Deep Learning for the Analysis of Magnetic Resonance Image in Neuro-Oncology. Cancers, 14(2): 300. open access.

G Ungan, A Pons-Escoda, D Ulinic, C Arús, S Ortega-Martorell, A Vellido, C Majós, M Julià-Sapé (2023) Metabolic pattern recognition of contrast-enhancing lesions in glioblastomas one month after concomitant therapy. NMR in Biomedicine, accepted.

JM López-Correa, C König, A Vellido (2023) GPCR molecular dynamics forecasting using recurrent neural networks. Scientific Reports, 13:20995

M Hueso, R Álvarez, D Marí, V Ribas-Ripoll, K Lekadir, A Vellido (2023) Is generative artificial intelligence the next step toward a personalized hemodialysis? Revista de Investigación Clínica - Clinical and Translational Investigation. doi: 10.24875/RIC.23000162. Epub ahead of print

A Benali, R Martin-Pinardel, E Romero, A Vellido (2023) Optimización de Imágenes. En Inteligencia Artificial y Datos de Imagen. Procesamiento de Imágenes (capitulo). Inteligencia Artificial y Oftalmología: Estado Actual en Cataluña. Annals d’Oftalmologia 31(4):198-205

M Hueso, N Rotllan, JC Escolà-Gil, and A Vellido (2023) Editorial: Systems biology and data-driven machine learning-based models in personalized cardiovascular medicine. Frontiers in Cardiovascular Medicine, 10:1320110. doi: 10.3389/fcvm.2023.1320110

G Ungan, C Arús, A Vellido, M Julià-Sapé (2023) A Comparison of Non-Negative Matrix Underapproximation Methods for the Decomposition of Magnetic Resonance Spectroscopy Data from Human Brain Tumors. NMR in Biomedicine, 36(12): e5020. open access

C Pitarch, V Ribas, A Vellido (2023) AI-Based Glioma Grading for a Trustworthy Diagnosis: An Analytical Pipeline for Improved Reliability. Cancers, 15(13), 3369.

G Ungan, A Pons-Escoda, D Ulinic, C Arús, A Vellido, M Julià-Sapé (2023) Using Single-Voxel Magnetic Resonance Spectroscopy Data Acquired at 1.5T to Classify Multivoxel Data at 3T: A Proof-of-Concept Study. Cancers, 15(14), 3709.

PJG Lisboa, S Saralajew, A Vellido, R Fernández-Domenech, T Villmann (2023) The Coming of Age of Interpretable and Explainable Machine Learning Models. Neurocomputing, 535, 25-39, doi.

MA Gutiérrez Mondragón, C König, A Vellido (2023) Layer-wise Relevance Analysis for Motif Recognition in the Activation Pathway of the β2-adrenergic GPCR Receptor. International Journal of Molecular Sciences, 24(2): 1155.

L Carrera-Escalé, A Benali, A-C Rathert, R Martín-Pinardel, A Alé-Chilet, M Barraso, C Bernal-Morales, S Marín-Martinez, S Feu-Basilio, J Rosinés-Fonoll, T Hernandez, I Vilá, C Oliva, I Vinagre, E Ortega, M Gimenez, E Esmatjes, A Vellido, E Romero and J Zarranz-Ventura (2023) Radiomics-Based Assessment of OCT Angiography Images for Diabetic Retinopathy Diagnosis, Ophthalmology Science, 3(2), 100259. doi.org/10.1016/j.xops.2022.100259

S Sánchez-Martínez, O Camara, G Piella, M Cikes, MA González Ballester, M Miron, A Vellido, E Gómez Gutiérrez, AG Fraser and B Bijnens. Machine learning for clinical decision-making: challenges and opportunities in cardiovascular imaging. Frontiers in Cardiovascular Medicine - Cardiovascular Imaging, 8, p2020, 2022

Y Hernández-Villegas, S. Ortega-Martorell, C. Arús, A. Vellido, M. Julià-Sapé (2022) Extraction of artefactual MRS patterns from a large database using  non-negative matrix factorization. NMR in Biomedicine; 35(4):e4193. https://doi.org/10.1002/nbm.4193

A Vellido, C. Angulo, K. Gibert A self-organizing world: special issue of the 13th edition of the workshop on self-organizing maps and learning vector quantization, clustering and data visualization, WSOM+ 2019, Neural Computing & Applications, 34, 1-3, 2022 DOI:10.1007/s00521-021-06307-w

LM Núñez, E Romero, M Julià-Sapé, MJ Ledesma-Carbayo, A Santos, C Arús, AP Candiota, and A Vellido. (2020) Unraveling response to temozolomide in preclinical GL261 glioblastoma with MRI/MRSI using radiomics and signal source extraction. Scientific Reports 10:19699. https://doi.org/10.1038/s41598-020-76686-y

Vellido, A. (2020) The importance of interpretability and visualization in Machine Learning for applications in medicine and health care. Neural Computing & Applications 32, 18069-18083. draft.

Sanchez-Martinez, S., Camara, O., Piella, G., Cikes, M., Gonzalez Ballester, M.A., Miron, M., Vellido, A., Gomez, E., Fraser, A., Bijnens, B (2019) Machine Learning for clinical decision-making: challenges and opportunities. Preprints, 2019110278 (doi: 10.20944/preprints201911.0278.

Ribas Ripoll, V., Vellido, A (2019) Blood pressure assessment with differential pulse transit time and deep learning: a proof of concept. Kidney Diseases, 5(1): 23-27. DOI: 10.1159/000493478

Hueso M, Vellido A (2019) Artificial Intelligence and Dialysis. Kidney Diseases, 5(1): 1-2. DOI: 10.1159/000493933

Vellido A (2019) Societal Issues Concerning the Application of Artificial Intelligence in Medicine. Kidney Diseases, 5(1): 11-17. free access https://doi.org/10.1159/000492428.

Aushev, A., Ribas Ripoll, V., Vellido, A., Aletti, F., Bollen Pinto, B., Herpain, A., Hendrik Post, E. Romay Medina, E., Ferrer, R., Baselli, G., Bendjelid, K. (2018) Feature selection for the accurate prediction of septic and cardiogenic shock ICU mortality in the acute phase. PLoS ONE, 13(11): e0199089. https://doi.org/10.1371/journal.pone.0199089.

König, C., Shaim, I. , Vellido, A., Romero, E., Alquézar, R., Giraldo, J. (2018) Using machine learning tools for protein database biocuration assistance, Scientific Reports, 8:10148.

Hueso M, Vellido A, Montero N, Barbieri C, Ramos R, Angoso M, Cruzado JM, Jonsson A (2018) Artificial Intelligence for the Artificial Kidney: Pointers to the Future of a Personalized Hemodialysis Therapy. Kidney Diseases; 4:1-9. open access.

Vellido A, Ribas V, Subirats L, Morcales C, Ruiz Sanmartín A, Ruiz Rodríguez JC (2018) Machine Learning for Critical Care: State-of-the-Art and a Sepsis Case Study, BioMedical Engineering OnLine, 17(S1):135.

König C, Alquézar R, Vellido A, Giraldo J (2018) Systematic analysis of primary sequence domain segments for the discrimination between class C GPCR subtypes, Interdisciplinary Sciences: Computational Life Sciences, 10(1), 43-52.

A. Shkurin, A. Vellido (2017) Using random forests for assistance in the curation of G-protein coupled receptor databases. BioMedical Engineering OnLine, 16(Suppl 1):75

D.L. García, À. Nebot, A. Vellido (2017) Intelligent data analysis approaches to churn as a business problem: a survey. Knowledge and Information Systems, 51(3):719-774. DOI: 10.1007/s10115-016-0995-z  (draft)

X. Cipriano, A. Vellido, J. Cipriano, J. Martí, S. Danov (2017) Application of clustering and simulation methods for the analysis of socio-economical and technical factors influencing the energy refurbishment of neighbourhoods. Energy Efficiency, 10(2), 359-382. doi: 10.1007/s12053-016-9460-9

C.L. König, M.I. Cárdenas, J. Giraldo, R. Alquezar, A. Vellido (2015) Label noise in subtype discrimination of class C G-protein coupled receptors: A systematic approach to the analysis of classification errors. BMC Bioinformatics, 16(1):314. open access

P.J.G. Lisboa, J.D. Martín, and A. Vellido (2015) Making Nonlinear Manifold Learning Models Interpretable: the Manifold Grand Tour. Expert Systems with Applications, 42(22), 8982-8988.

M.I. Cárdenas, A. Vellido, C. König, R. Alquézar and J. Giraldo (2015) Visual Characterization of Misclassified Class C GPCRs through Manifold-based Machine Learning Methods. Genomics and Computational Biology, Accepted

R.  Cruz-Barbosa, A. Vellido, J. Giraldo (2015) The influence of alignment-free sequence representations on the semi-supervised classication of Class C G Protein-Coupled Receptors. Medical & Biological Engineering & Computing, 53(2), 137-149, available online from Nov 2014.

C. König, R. Alquézar, A. Vellido and J. Giraldo (2014) Finding class C GPCR subtype-discriminating n-grams through feature selection. Journal of Integrative Bioinformatics, 11(3):254. doi: 10.2390/biecoll-jib-2014-254..

V. Ribas, A. Vellido, E. Romero and J.C. Ruiz-Rodríguez (2014) Sepsis Mortality Prediction with Quotient Basis Kernels. Artificial Intelligence in Medicine, 61(1), 45-52. DOI:10.1016/j.artmed.2014.03.004

C. Arizmendi, D.A. Sierra, A. Vellido and E.Romero (2014) Automated classification of brain tumours from short echo time in vivo MRS data using Gaussian decomposition and Bayesian Neural Networks, Expert Systems with Applications, 41(11), 5296-5307. doi: http://dx.doi.org/10.1016/j.eswa.2014.02.031.

Ortega-Martorell, S., Ruiz, H., Vellido, A., Olier, I., Romero, E., Julià-Sapé, M., Martín, J.D. , Jarman, I.H., Arús, C., Lisboa, P.J.G. (2013) A novel semi-supervised methodology for extracting tumor type-specific MRS sources in human brain data, PLoS ONE, 8(12): e83773.

Vilamala, A., Lisboa, P.J.G., Ortega-Martorell, S., Vellido, A. (2013) Discriminant Convex Non-negative Matrix Factorization for the Classification of Human Brain Tumours, Pattern Recognition Letters, 34(14), 1734–1747.

Cruz-Barbosa, R. and Vellido, A. (2013) Generative manifold learning for the exploration of partially labeled data. Computación & Sistemas, 17(4), 641-653. doi: 10.13053/CyS-17-4-2013-014

Vellido, A., García, D., Nebot, À. (2013) Cartogram Visualization for Nonlinear Manifold Learning Models. Data Mining and Knowledge Discovery, 27(1):22-54, doi: 10.1007/s10618-012-0294-6

Ortega-Martorell, S., Lisboa, P.J.G., Vellido, A., Simões, R.V., Pumarola, M., Julià-Sapé, M., Arús, C. (2012) Convex Non-Negative Matrix Factorization for Brain Tumor Delimitation from MRSI Data, PLoS ONE, 7(10):e47824.

Ortega-Martorell, S., Lisboa, P.J.G., Vellido, A.,Julià-Sapé, M., Arús, C. (2012) Non-negative Matrix Factorisation methods for the spectral decomposition of MRS data from human brain tumours. BMC Bioinformatics, 13:38. (draft)

Vellido, A., Romero, E., Julià-Sapé, M., Majós, C., Moreno-Torres, À., and Arús, C. (2012) Robust discrimination of glioblastomas from metastatic brain tumors on the basis of single-voxel proton MRS. NMR in Biomedicine. 25(6):819–828.

Ribas, V.J., Vellido, A., Ruiz-Rodríguez, J.C., Rello, J. (2012) Severe sepsis mortality prediction with logistic regression over latent factors. Expert Systems with Applications, 39(2), 1937-1943.

Arizmendi, C., Vellido, A., Romero, E. (2012) Classification of human brain tumours from MRS data using discrete wavelet transform and Bayesian neural networks. Expert Systems with Applications, 39(5), 5223-5232.

Olier, I., Amengual, J. and Vellido, A. (2011) A variational Bayesian approach for the robust estimation of cortical silent periods from EMG time series of brain stroke patients. Neurocomputing, 74(9): 1301-1314.

Cruz, R., Vellido, A., (2011) Semi-supervised analysis of human brain tumours from partially labeled MRS information, using manifold learning models. International Journal of Neural Systems. 21(1): 17-29.

Lisboa, P.J.G.  Vellido, A.  Tagliaferri, R.  Napolitano, F.  Ceccarelli, M.  Martin-Guerrero, J.D.  Biganzoli, E. (2010) Data Mining in Cancer Research, IEEE Computational Intelligence Magazine, 5(1), 14-18

Cruz, R., Vellido, A. (2010) Semi-Supervised Geodesic Generative Topographic Mapping. Pattern Recognition Letters, 31(3), 202-209.

González-Navarro, F.F., Belanche-Muñoz, Ll.A., Romero, E., Vellido, A., Julià-Sapé, M., Arús, C. (2010) Feature and model selection with discriminatory visualization for diagnostic classification of brain tumours.  Neurocomputing, 73(4-6), 622-632.

Vellido, A., Romero, E., González-Navarro, F.F., Belanche-Muñoz, Ll., Julià-Sapé, M., Arús, C. (2009) Outlier exploration and diagnostic classification of a multi-centre ¹H-MRS brain tumour database. Neurocomputing, 72(13-15), 3085-3097.

Schleif, F.-M., Biehl, M., and Vellido, A. (2009) Advances in machine learning and computational intelligence. Neurocomputing, 72(7-9), 1377-1378.

Olier, I., Vellido, A. (2008) Variational Bayesian Generative Topographic Mapping. Journal of Mathematical Modelling and Algorithms, 7(4), 371-387.

Olier, I., Vellido, A. (2008) Advances in Clustering and Visualization of Time Series using GTM Through Time. Neural Networks, 21(7), 904-913. preprint. 

Vellido, A., Andrade, A.O. (2007) Determination of feature relevance for the grouping of motor unit action potentials through a generative mixture model. Biomedical Signal Processing and Control, 2(2), 111-121.

Vellido, A., Martí, E., Comas, J., Rodríguez-Roda, I., and Sabater, F. (2007) Exploring the ecological status of human altered streams through Generative Topographic Mapping. Environmental Modelling & Software, 22(7), 1053-1065. (download).

Vellido, A., (2006) Missing data imputation through GTM as a mixture of t-distributions. Neural Networks, 19(10), 1624-1635. (download)

Vellido, A., Lisboa, P.J.G., and Vicente, D. (2006) Robust analysis of MRS brain tumour data using t-GTM. Neurocomputing, 69(7-9), 754-768. (pdf)

Vellido, A. and Lisboa, P.J.G. (2006) Handling outliers in brain tumour MRS data analysis through robust topographic mapping, Computers in Biology and Medicine, 36(10), 1049-1063. (download)

Vellido, A., El-Deredy, W., and Lisboa, P.J.G. (2003) Selective Smoothing of the Generative Topographic Mapping. IEEE Transactions on Neural Networks, 14(4), 847-852

Vellido, A and Lisboa, P.J.G. (2001) An electronic commerce application of the Bayesian Framework for MLPs: the Effect of Marginalization and ARD. Neural Computing & Applications, 10(1)

Vellido, A., Lisboa, P.J.G. and Meehan, K. (2000) The Generative Topographic Mapping as a principled model for data visualization and market segmentation: an electronic commerce case study. International Journal of Computers, Systems, and Signals. 1(2), 119-138.

Vellido, A, Lisboa, P.J.G and Meehan, K. (2000) Quantitative characterization and prediction of on-line purchasing behaviour: a latent variable approach. International Journal of Electronic Commerce, 4(4), 83-104.

Lisboa. P.J.G., Vellido, A. and Wong, H. (2000) Bias reduction in skewed binary classification with Bayesian neural networks. Neural Networks. 13, 407-410.

Vellido, A., Lisboa, P.J.G and Vaughan, J. (1999) Neural networks in business: a survey of applications (1992-1998) Expert Systems with Applications. 17, 51-70.

Vellido, A., Lisboa, P.J.G and Meehan, K. (1999) Segmentation of the on-line shopping market using neural networks. Expert Systems with Applications. 17, 303-314.

Lisboa, P.J.G., Kirby, S.J.P., Vellido, A., Lee, Y.Y.B. and El-Deredy, W. (1998) Assessment of Statistical and Neural Network Methods in NMR Spectral Classification and Metabolite Selection. Nuclear Magnetic Resonance in Biomedicine. 11, 225-234.

Conferences, technical reports and newsletters

C König and A Vellido (2024) Exploring data distributions in Machine Learning models with SOMs. In International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization (WSOM+ 2024). Accepted for publication.

M Hueso, RI Rodríguez Urquía, R Alvarez Esteban, M Quero, GE Villalobos, C Galofre, R Peiró-Jordán, DM Martínez, A Vellido (2023) Empowering Dialysis Patients to Self-Manage Hyperphosphatemia with Mobile Health Technology: Insights from the FOSFO-OK study. American Society of Nephrology (ASN) Kidney Week Meeting, 2023.

G Ungan, A Pons, D Ulinic, C Arús, A Vellido, M Julià-Sapé (2023) Nosological images of brain tumor MV-MRS 3T data based on classifiers trained with SV-MRS 1.5T data, a proof-of-concept. Annual Meeting of the International Society for Magnetic Resonance in Medicine (ISMRM'23)

MA Gutiérrez Mondragón, C König, A Vellido (2023) Recognition of conformational states of a G Protein-Coupled Receptor from molecular dynamic simulations using sampling techniques. In I Rojas, O Valenzuela, F Rojas, LJ Herrera and F Ortuño (Eds.)  Procs. of the 10th International Work-Conference on Bioinformatics and Biomedical Engineering, Part I,  IWBBIO 2023, LNBI 13919, Springer, pp.3-16.

J Zarranz-Ventura, L Carrera-Escale, A Benali, A-C Rathert, R Martín-Pinardel, A Ale-Chilet, M Barraso, C Bernal-Morales, S Marín-Martínez, S Feu-Basilio, J Rosines-Fonoll, T Hernández, I Vila, R Castro, C Oliva, I Vinagre, E Ortega, M Giménez, A Vellido, E Romero (2022) Radiomics-based assessment of multimodal retinal imaging techniques for diabetes mellitus and diabetic retinopathy diagnosis. In: 22nd EURETINA Congress, Hamburg, Germany (abstract)

JM Lopez Correa, C König, A Vellido (2022) Molecular Dynamics forecasting of transmembrane Regions in GPCRs by Recurrent Neural Networks, 2022 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI), pp.1-4, doi: 10.1109/BHI56158.2022.9926945.

JM López Correa, C König, A Vellido (2022) Long Short-Term Memory to predict 3D Amino acids Positions in GPCR Molecular Dynamics, In Artificial Intelligence Research and Development, Proceedings of the 24th International Conference of the Catalan Association for Artificial Intelligence  CCIA 2022, Frontiers in Artificial Intelligence and Applications series, IOS Press, pp.209-220.

A Benali, L Carrera, A Christin, R Martín, A Alé, M Barraso, C Bernal, S Marín, S Feu, J Rosinés, T Hernández, I Vilá, C Oliva, I Vinagre, E Ortega, M Giménez, E Esmatjes, J Zarranz-Ventura, E Romero, A Vellido (2022) NMF for quality control of multi-modal retinal images for diagnosis of diabetes mellitus and diabetic retinopathy, In I Rojas, O Valenzuela, F Rojas, LJ Herrera and F Ortuño (Eds.)  Procs. of the 9th International Work-Conference on Bioinformatics and Biomedical Engineering, Part I,  IWBBIO 2022, LNBI 13346, Springer, pp.343-356.

MA Gutiérrez-Mondragón, C König, A Vellido (2022) A Deep Learning-based method for uncovering GPCR ligand-induced conformational states using interpretability techniques, In I Rojas, O Valenzuela, F Rojas, LJ Herrera and F Ortuño (Eds.)  Procs. of the 9th International Work-Conference on Bioinformatics and Biomedical Engineering, Part I,  IWBBIO 2022, LNBI 13346, Springer, pp. 275-287.

S Cavallaro, A Vellido, C König (2022) Visual insights from the latent space of generative models for molecular design, In Advances in Self-Organizing Maps, Learning Vector Quantization, Clustering and Data Visualization. Dedicated to the Memory of Teuvo Kohonen / Proceedings of the 14th International Workshop, WSOM+ 2022, pp.108-117.

GS Ungan, A Pons Escoda, D Ulinic, C. Arús, A Vellido, C Majós and M Julià-Sapé (2022) MRSI-detected pattern in glioblastoma patients one month after concomitant chemoradiotherapy, Proceeding of the International Society for Magnetic Resonance in Medicine, ISMRM, 30, 0840.

AX Astudillo Aguilar, S Rosso, K Gibert, A Vellido, Visual mining of industrial gas turbines sensor data as an industry 4.0 application. In 16th International Conference on Soft Computing Models in Industrial and Environmental Applications, SOCO 2021, H Sanjurjo González et al. (eds) Advances in Intelligent Systems and Computing, vol.1401, Springer, pp.101-111.

P Lisboa, S Saralajew, A Vellido and T Villmann (2021) The coming of age of interpretable and explainable machine learning models. In Proceedings of the 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2021)  pp.547-556. (link)

A Vellido (2021) The importance of interpretability and visualization in ML for medical applications, II IABiomed Workshop, CEDI 2021, CAEPIA, Málaga.

Núñez, L.M., Julià-Sapé, M., Romero, E.,  Arús, C., Vellido, A. and Candiota, A.P. (2020) Monitoring of TMZ therapy in murine GL261 glioblastoma by combining Radiomics, Convex-NMF and feature selection in MRI/MRSI data analysis. L01.78 in ESMRMB 2020 Online, 37th Annual Scientific Meeting, September 30–October 2: Lightning Talks / Electronic Posters / Clinical Review Posters / Software Exhibits. Magnetic Resonance Materials in Physics, Biology and Medicine 33, 69–233. https://doi-org.recursos.biblioteca.upc.edu/10.1007/s10334-020-00876-y

Núñez, LM, Julià-Sapé, M, Romero, E, Arús, C, Vellido, A, Candiota, AP. (2020) Interpreting response to TMZ therapy in murine GL261 glioblastoma by combining Radiomics, Convex-NMF and feature selection in MRI/MRSI data analysis. In 2020 International Joint Conference on Neural Networks (IJCNN), Glasgow, United Kingdom, pp. 1-8, doi: 10.1109/IJCNN48605.2020.9207031

Núñez, LM, Julià-Sapé, M, Romero, E, Arús, C, Vellido, A, Candiota, AP (2020) Interpreting response to TMZ therapy in murine GL261 glioblastoma by combining Radiomics, Convex-NMF and feature selection in MRI/MRSI data analysis. International Joint Conference on Neural Networks (IJCNN 2020), accepted.

Bazaga, A, Ofir-Rosenfeld, Y, Rausch, O, Vellido, A, Weisser, H, Sullivan, J (2019) Genome-wide investigation of gene-cancer associations using machine learning on biomedical big data for the prediction of novel therapeutic targets. In ISMB/ECCB 2019.

Bazaga, A, Vellido, A (2019) Network Community Cluster-Based Analysis for the Identification of Potential Leukemia Drug Targets. In Procs. of the International Workshop on Self-Organizing Maps (WSOM+19), pp. 314-323. Springer.

Bacciu, D., Biggio, B., Lisboa, P.J.G., Martín, J.D., Oneto, L., Vellido, A. (2019) Societal issues in Machine Learning: when learning from data is not enough. In Proceedings of the 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2019), Bruges, Belgium, pp.

Bilal, A., Vellido, A., Ribas, V. (2018) Enabling interpretation of the outcome of a human obesity prediction machine learning analysis from genomic data. In procs. of the 15th International Conference on Computational Intelligence methods for Bioinformatics and Biostatistics (CIBB 2018) Lisbon, Portugal.

Bacciu, D., Lisboa, P.J., Martín, J.D., Stoean, R. and Vellido, A. (2018) Bioinformatics and medicine in the era of Deep Learning. In Proceedings of the 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2018), Bruges, Belgium, pp.345-354.

Bilal, A., Vellido, A., Ribas, V. (2018) Big data analytics for obesity prediction. 21st International Conference of the Catalan Association for Artificial Intelligence (CCIA 2018) Roses, Spain. In Falomir, Z., Gibert, K. and Plaza, E., eds. Frontiers in Artificial Intelligence and Applications, vol.308, pp.141-145, IOS Press.

Y. Hernández–Villegas, V. Mocioiu, D. Ulinic, S.P. Kyathanahally, A. Vellido, C. Arús, M. Julià-Sapé (2017) Automated quality control of magnetic resonance spectra of brain tumors by Convex Non-negative Matrix Factorization. In 34th Annual Scientific Meeting of the European Society for Magnetic Resonance in Medicine and Biology (ESMRMB), Barcelona, Spain.

C. König, R. Alquézar, A. Vellido, J. Giraldo (2017) Discovering subtype specific n-gram motifs in class C GPCR N-termini. In Recent Advances in Artificial Intelligence Research and Development: Proceedings of the 20th International Conference of the Catalan Association for Artificial Intelligence, Deltebre, Terres de L'Ebre, Spain, October 25-27, 2017 (Vol. 300, p. 116). IOS Press

A. Vellido, D.L. García (2017) Electricity rate planning for the current consumer market scenario through segmentation of consumption time series. In Artificial Intelligence in Power and Energy Systems (AIPES) 18th EPIA Conference on Artificial Intelligence, Porto, Portugal, LNCS 10423, pp. 295-306, Springer
C. König, R. Alquézar, A. Vellido, J. Giraldo (2017) Topological sequence segments discriminate between class C GPCR subtypes. In 11^th International Conference on Practical Applications of Computational Biology & Bioinformatics (PACBB 2017), Porto, Portugal. Accepted

A. Vellido, V. Ribas, C. Morales, A. Ruiz Sanmartín, J.C. Ruiz-Rodríguez (2017) Machine Learning for Critical Care: An Overview and a Sepsis Case Study. In I. Rojas and F. Ortuño (Eds.): Procs. of the 5^th International Work-Conference on Bioinformatics and Biomedical Engineering (IWBBIO 2017), Granada, Spain. Part I, LNBI 10208, pp.15-30, Springer. doi: 10.1007/978-3-319-56148-6_2.