Ungan, G, Pons-Escoda, A, Ulinic, D, Arús, C, Ortega-Martorell, S, Olier, I, Vellido, A, Majós, C and Julià-Sapé, M (2024) Early pseudoprogression and progression lesions in glioblastoma patients are both metabolically heterogeneous. NMR in Biomedicine. pp. 1-12. ISSN 0952-3480

Lisboa, P, Saralajew, S, Vellido, A, Fern´andez-Domenech, R and Villmann, T (2023) The coming of age of interpretable and explainable machine learning models. Neurocomputing, 535. pp. 25-39. ISSN 0925-2312

Lisboa, PJG, Saralajew, S, Vellido, A and Villmann, T (2021) The Coming of Age of Interpretable and Explainable Machine Learning Models. In: ESANN 2021 proceedings European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning . (ESANN 2021 - European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, 06 October 2021 - 08 October 2021, Online).

Hernández-Villegas, Y, Ortega Martorell, S, Arus, C, Vellido, A and Julia-Sape, M (2019) Extraction of artefactual MRS patterns from a large database using non-negative matrix factorization. NMR in Biomedicine. ISSN 0952-3480

Mocioiu, V, De Barros, NMP, Ortega-Martorell, S, Slotboom, J, Knecht, U, Arús, C, Vellido, A and Julià-Sapé, M (2016) A machine learning pipeline for supporting differentiation of glioblastomas from single brain metastases. In: ESANN 2016 - 24th European Symposium on Artificial Neural Networks , ES2016 (82). (ESANN 2016 - 24th European Symposium on Artificial Neural Networks, 27th - 29th April 2016, Bruges, Belgium).

Lisboa, P, Martín-Guerrero, JD and Vellido, A (2015) Making nonlinear manifold learning models interpretable: The manifold grand tour. Expert Systems with Applications, 42 (22). pp. 8982-8988. ISSN 0957-4174

Ortega-Martorell, S, Ruiz, H, Vellido, A, Olier, I, Romero, E, Julia-Sape, M, Martin, JD, Jarman, I, Arus, C and Lisboa, P (2013) A Novel Semi-Supervised Methodology for Extracting Tumor Type-Specific MRS Sources in Human Brain Data. PLOS ONE, 8 (12). pp. 1-14. ISSN 1932-6203

Vellido, A (2000) A methodology for the characterization of business-to-consumer E-commerce. Doctoral thesis, Liverpool John Moores University.

Bacciu, D, Lisboa, PJG, Martín, JD, Stoean, R and Vellido, A Bioinformatics and Medicine in the Era of Deep Learning. In: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, 25-27th April 2018, Bruges, Belgium. (Accepted)