Pedro Larrañaga

Pedro Larrañaga is Full Professor in Computer Science and Artificial Intelligence at the Technical University of Madrid (UPM) since 2007. He received the MSc degree in mathematics (statistics) from the University of Valladolid and the PhD degree in computer science from the University of the Basque Country (“excellence award”). Before moving to UPM, his academic career has been developed at the University of the Basque Country (UPV-EHU) at several faculty ranks: Assistant Professor (1985-1998), Associate Professor (1998-2004) and Full Professor (2004-2007). He earned the habilitation qualification for Full Professor in 2003.

His research interests are primarily in the areas of probabilistic graphical models, metaheuristics for optimization, data mining, classification models, and real applications, like biomedicine, bioinformatics, neuroscience, industry 4.0 and sports. He has published more than 200 papers in impact factor journals and has supervised 36 PhD theses. He has been recognized as a Fellow by several prestigious organizations: the European Association for Artificial Intelligence (2012) , Academia Europaea (2018), the Asia-Pacific Artificial Intelligence Association (2021), Jakiunde—Academy for the Sciences, Arts, and Letters of the Basque Country (2022), the European Laboratory for Learning and Intelligent Systems (ELLIS) (2023), IEEE (2023), and the Industry Academy within the International Artificial Intelligence Industry Alliance (2024). His accolades include the Spanish National Prize in Computer Science (2013) , the Spanish Association for Artificial Intelligence Prize (2018), and the Amity Research Award in Machine Learning (2020).​

2024

• Blasco T, Balzerani F, Valcarcel L, Larrañaga P, Bielza C, Francino M, Rufián-Henares J, Planes F, Pérez-Burillo S (2024). BN-BacArena: Bayesian network extension of bacArena for the dynamic simulation of microbial communities. Bioinformatics, 40(5), btae266.
• Larrañaga P, Bielza C (2024). Estimation of Distribution Algorithms in Machine Learning: A Survey. IEEE Transactions on Evolutionary Computation, 28(5), 1301-1321.
• Puerto-Santana C, Larrañaga P, Bielza C (2024). Feature saliencies in asymmetric hidden Markov models. IEEE Transactions on Neural Networks and Learning Systems, 35, 3, 3586-3600.
• Quesada D, Larrañaga P, Bielza C (2024). dbnR: Gaussian dynamic Bayesian network learning and inference in R. Journal of Statistical Software, 5126.
• Soloviev VP, Larrañaga P, Bielza C (2024). EDAspy: An extensible python package for estimation of distribution algorithms. Neurocomputing, 558, 128043.
• Soloviev VP, Bielza C, Larrañaga P (2024). Semiparametric estimation of distribution algorithms for continuous optimization. IEEE Transactions on Evolutionary Computation, 28(4), 1069-1083.
• Soloviev VP, Bielza C, Larrañaga P, Dunjko V, Wang H (2024). Trainability maximization using estimation of distribution algorithms assisted by surrogate modelling for quantum architecture search. EPJ Quantum Technol. 11, 69.

2023

• Bernaola N, Michiels M,Larrañaga P, Bielza C (2023). Learning massive interpretable gene regulatory networks of the human brain by merging Bayesian networks. PLOS Computational Biology, 19(12): e1011443.
• Larrañaga P, Bielza C (2023). Estimation of Distribution Algorithms in Machine Learning: A Survey. IEEE Transactions on Evolutionary Computation, in press.
• Puerto-Santana C, Larrañaga P, Bielza C (2023). Feature subset selection in data-stream environments using asymmetric hidden Markov models and novelty detection. Neurocomputing, 554, 126641.
• Soloviev VP, Bielza C, Larrañaga P (2023). Quantum approximate optimization algorithm for Bayesian network structure learning. Quantum Information Processing, 22:19, 1-28.
• Soloviev VP, Bielza C, Larrañaga P (2023). Semiparametric estimation of distribution algorithms for continuous optimization. IEEE Transactions on Evolutionary Computation, in press.
• Valero-Leal E, Bielza C, Larrañaga P, Renooij S (2023). Efficient search for relevance explanations using MAP-independence in Bayesian networks. International Journal of Approximate Reasoning, 160, 108965.
• Valverde G, Quesada D, Larrañaga P, Bielza C (2023). Causal reinforcement learning based on Bayesian networks applied to industrial settings. Engineering Applications of Artificial Intelligence, 125, 106657.
• Villa-Blanco C, Bielza C, Larrañaga P (2023). Feature subset selection for data and feature streams: A review. Artificial Intelligence Review, 56, 1011-1062
• Villa-Blanco C, Bregoli A, Bielza C, Larrañaga P, Stella F (2023). Constraint-based and hybrid structure learning of multidimensional continuous-time Bayesian network classifiers. International Journal of Approximate Reasoning, 159, 108945.

2022

• Atienza D, Bielza C, Larrañaga P (2022). PyBNesian: An extensible python package for Bayesian networks. Neurocomputing, 504, 204-209.
• Atienza D, Bielza C, Larrañaga P (2022). Semiparametric Bayesian networks. Information Sciences, 584, 564-582.
• Atienza D, Larrañaga P, Bielza C (2022). Hybrid semiparametric Bayesian networks. TEST, 31, 299-327.
• Atienza D, Larrañaga P, Bielza C (2022). Rejoinder on: Hybrid semiparametric Bayesian networks. TEST, 31 (2), 344-347.
• Laccourreye P, Bielza C, Larrañaga P (2022). Explainable machine mearning for longitudinal multi-omic microbiome. Mathematics, 10 (12), 1994.
• Puerto-Santana C, Bielza C, Diaz-Rozo J, Ramirez-Gargallo G, Mantovani F, Virumbrales G, Labarta J, Larrañaga P (2022). Asymmetric HMMs for online ball-bearing health assessments. IEEE Internet of Things Journal, 9(20), 20160-20177.
• Puerto-Santana C, Larrañaga P, Bielza C (2022). Autoregressive asymmetric linear Gaussian hidden Markov models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44,9,4642-4658.
• Quesada D, Bielza C, Fontán P, Larrañaga P (2022). Piecewise forecasting of nonlinear time series with model tree dynamic Bayesian networks. International Journal of Intelligent Systems, 37, 9108-9137.
• Rodriguez-Sanchez F, Bielza C, Larrañaga P (2022). Multipartition clustering of mixed data with Bayesian networks. International Journal of Intelligent Systems, 37, 2188-2218.
• Soloviev VP, Larrañaga P, Bielza C (2022). Estimation of distribution algorithms using Gaussian Bayesian networks to solve industrial optimization problems constrained by environment variables. Journal of Combinatorial Optimization, 44 (2), 1077-1098.

2021

• Atienza D, Bielza C, Díaz-Rozo J, Larrañaga P (2021). Efficient anomaly detection in a laser surface heat treatment process by tracking the laser spot. IEEE/ASME Transactions on Mechatronics, 26(1), 405-415.
• Benjumeda M, Tan YL, Gonzalez-Otárula KA, Chandramohan D, Chang EF, Hall JA, Bielza C, Larrañaga P, Kobayashi E, Knowlton RC (2021). Patient specific prediction of temporal lobe epilepsy surgical outcomes. Epilepsia, 62 (9), 2113-2122.
• Gil-Begue S, Bielza C, Larrañaga P (2021). Multi-dimensional Bayesian network classifiers: A survey. Artificial Intelligence Review, 54(1), 519–559.
• Michiels M, Larrañaga P, Bielza C (2021). BayeSuites: An open web framework for massive Bayesian networks focused on neuroscience. Neurocomputing, 428, 166-181.
• Mihaljević B, Bielza C, Larrañaga P (2021). Bayesian networks for interpretable machine learning and optimization. Neurocomputing, 456, 648-665.
• Mihaljević B, Larrañaga P, Bielza C (2021). Comparing the electrophysiology and morphology of human and mouse layer 2/3 pyramidal neurons with Bayesian networks. Frontiers in Neuroinformatics, 15, 580873.
• Quesada D, Valverde G, Larrañaga P, Bielza C (2021). Long-term forecasting of multivariate time series in industrial furnaces with dynamic Gaussian Bayesian networks. Engineering Applications of Artificial Intelligence, 103, 104301.
• Rodriguez-Sanchez F, Rodriguez-Blazquez C, Bielza C, Larrañaga P (2021). Identifying Parkinson’s disease subtypes with motor and non-motor symptoms via model-based multi-partition clustering. Scientific Reports, 11 (1), 1-10.
• Villa-Blanco C, Larrañaga P, Bielza C (2021). Multidimensional continuous time Bayesian network classifiers. International Journal of Intelligent Systems, 36 (12), 7839-7866.

2020

• Córdoba-Sánchez I, Bielza C, Larrañaga P, Varando G (2020). Sparse Cholesky covariance parametrization for recovering latent structure in ordered data. IEEE Access, 8, 154614-154624.
• Córdoba-Sánchez I, Bielza C, Larrañaga P (2020). A review of Gaussian Markov models for conditional independence. Journal of Statistical Planning and Inference, 206, 127-144.
• Córdoba-Sánchez I, Varando G, Bielza C, Larrañaga P (2020). On generating random Gaussian graphical models. International Journal of Approximate Reasoning, 125, 240 - 250.
• Díaz-Rozo J, Bielza C, Larrañaga P (2020). Machine-tool condition monitoring with Gaussian mixture models-based dynamic probabilistic clustering", Engineering Applications of Artificial Intelligence, 89, 103434.
• Mihaljević B, Larrañaga P, Benavides-Piccione R, DeFelipe J, Bielza C (2020). Comparing basal dendrite branches in human and mouse hippocampal CA1 pyramidal neurons with Bayesian networks. Scientific Reports, 10 (1), 18592.
• Rodriguez-Sanchez F, Larrañaga P, Bielza C (2020). Incremental learning of latent forests. IEEE Access, 8, 224420-224432.

2019

• Benjumeda M, Bielza C, Larrañaga P (2019). Learning tractable Bayesian networks in the space of elimination orders. Artificial Intelligence, 274, 66-90.
• Benjumeda M, Luengo-Sanchez S, Larrañaga P, Bielza C (2019). Tractable learning of Bayesian networks from partially observed data. Pattern Recognition, 91, 190-199.
• Fernandez P, Bielza C, Larrañaga P (2019). Random forests for regression as a weighted sum of k-potential nearest neighbors. IEEE Access, 7(1), 25660-25672.
• Leguey I, Bielza C, Larrañaga P (2019). Circular Bayesian classifiers using wrapped Cauchy distributions. Data Knowledge Engineering, 122, 101-115.
• Leguey I, Larrañaga P, Bielza C, Kato S (2019). A circular-linear dependence measure under Johnson-Wehrly distributions and its application in Bayesian networks. Information Sciences, 486, 240-253.
• Luengo-Sanchez S, Larrañaga P, Bielza C (2019). A directional-linear Bayesian network and its application for clustering and simulation of neural somas. IEEE Access, 7(1), 69907-69921.
• Mihaljević B, Benavides-Piccione R, Bielza C, Larrañaga P, DeFelipe J (2019). Classification of GABAergic interneurons by leading neuroscientists. Scientific Data , 6, 221.

2018

• Anton-Sanchez L, Effenberger F, Bielza C, Larrañaga P, Cuntz H (2018). A regularity index for dendrites – local statistics of a neuron’s input space, PLoS Computational Biology, 14, 11, e1006593.
• Benjumeda M, Bielza C, Larrañaga P (2018). Tractability of most probable explanations in multidimensional Bayesian network classifiers. International Journal of Approximate Reasoning, 93, 74-87.
• Díaz-Rozo J, Bielza C, Larrañaga P (2018). Clustering of data streams with dynamic Gaussian mixture models. An IoT application in industrial processes. IEEE Internet of Things Journal, 5(5), 3533-3547.
• Luengo-Sanchez S, Fernaud-Espinosa I, Bielza C, Benavides-Piccione R, Larrañaga P, DeFelipe J (2018). 3D morphology-based clustering and simulation of human pyramidal cell dendritic spines. PLOS Computational Biology, 14(6), e1006221.
• Mihaljević B, Larrañaga P, Benavides-Piccione R, Hill S, DeFelipe J, Bielza C (2018). Towards a supervised classification of neocortical interneuron morphologies. BMC Bioinformatics, 19(1), 511.
• Mihaljević B, Bielza C, Larrañaga P (2018). bnclassify: Learning Bayesian network classifiers. The R Journal, 10(2), 455-468, RJ-2018-073.
• Varando G, Benavides-Piccione R, Muñoz A, Kastanauskaite A, Bielza C, Larrañaga P, DeFelipe J (2018). MultiMap: A tool to automatically extract and analyze spatial microscopic data from large stacks of confocal microscopy images. Frontiers in Neuroanatomy, 12(37).

2017

• Anton-Sanchez L, Larrañaga P, Benavides-Piccione R, Fernaud I, DeFelipe J, Bielza C (2017). Three-dimensional spatial modeling of spines along dendritic networks in human cortical pyramidal neurons, PLoS ONE, 12(6), e0180400.
• Anton-Sanchez L, Bielza C, Larrañaga P (2017). Network design through forests with degree and role-constrained minimum spanning trees. Journal of Heuristics, 23(1), 31-51.
• Fernandez-Gonzalez P, Benavides-Piccione R, Leguey I, Bielza C, Larrañaga P, DeFelipe J (2017). Dendritic branching angles of pyramidal neurons of the human cerebral cortex. Brain Structure and Function, 222(4), 1847-1859.
• Mu J, Chaudhuri KR, Bielza C, Pedro-Cuesta J, Larrañaga P, Martínez-Martín P (2017). Parkinson's disease subtypes identified from cluster analysis of motor and non-motor symptoms. Frontiers in Aging Neuroscience, 9(301).
• Rodriguez-Lujan L, Larrañaga P, Bielza C (2017). Frobenius norm regularization for the multivariate von Mises distribution. International Journal of Intelligent Systems, 32(2), 153–176.

2016

• Anton-Sanchez L, Bielza C, Benavides-Piccione R, DeFelipe J, Larrañaga P (2016). Dendritic and axonal wiring optimization of cortical GABAergic interneurons. Neuroinformatics, 14(4), 453-464.
• Anton-Sanchez L, Bielza C, Larrañaga P, DeFelipe J (2016). Wiring economy of pyramidal cells in the juvenile rat somatosensory cortex. PLoS ONE, 11(11), e0165915.
• Borchani H, Larrañaga P, Gama J, Bielza C (2016). Mining multi-dimensional concept-drifting data streams using Bayesian network classifiers. Intelligent Data Analysis, 20(2), 257-280.
• Leguey I, Bielza C, Larrañaga P, Kastanauskaite A, Rojo C, Benavides-Piccione R, DeFelipe J (2016). Dendritic branching angles of pyramidal cells across layers of the juvenile rat somatosensory cortex. Journal of Comparative Neurology, 524(13), 2567-2576.
• Leitner L, Bielza C, Hill SL, Larrañaga P (2016). Data publications correlate with citation impact. Frontiers in Neuroscience, 10(419).
• Rojo C, Leguey I, Kastanauskaite A, Bielza C, Larrañaga P, DeFelipe J, Benavides-Piccione R (2016). Laminar differences in dendritic structure of pyramidal neurons in juvenile rat somatosensory cortex. Cerebral Cortex, 26(6), 2811-2822.
• Varando G, Bielza C, Larrañaga P (2016). Decision functions for chain classifiers based on Bayesian networks for multi-label classification. International Journal of Approximate Reasoning, 68, 164-178.

2015

• Borchani H, Varando G, Bielza C, Larrañaga P (2015). A survey on multi-output regression. WIREs Data Mining and Knowledge Discovery, 5, 216--233. Highly cited paper.
• Karshenas H, Bielza C, Larrañaga P (2015). Interval-based ranking in noisy evolutionary multi-objective optimization. Computational Optimization and Applications, 61(2), 517-555.
• Larrañaga A, Bielza C, Pongrácz P, Faragó, T Bálint P, Larrañaga P (2015). Comparing supervised learning methods for classifying sex, age, context and individual Mudi dogs from barking. Animal Cognition, 18(2), 405-421.
• Lopez-Cruz PL, Bielza C, Larrañaga P (2015). Directional naive Bayes classifiers. Pattern Analysis and Applications, 18, 225-246.
• Luengo-Sanchez S, Bielza C, Benavides-Piccione R, Fernaud-Espinosa I, DeFelipe J, Larrañaga P (2015). A univocal definition of the neuronal soma morphology using Gaussian mixture models, Frontiers in Neuroanatomy, 9(137).
• Masegosa A, Armañanzas R, Grau MA, Potenciano V, Moral S, Larrañaga P, Bielza C, Matesanz F (2015). Discretization of expression quantitative trait loci in association analysis between genotypes and expression data. Current Bioinformatics, 10(2), 144-164.
• Mihaljević B, Benavides-Piccione R, Bielza C, DeFelipe J, Larrañaga P (2015). Bayesian network classifiers for categorizing cortical GABAergic interneurons. Neuroinformatics, 13(2), 192–208.
• Mihaljević B, Benavides-Piccione R, Guerra L, DeFelipe J, Larrañaga P, Bielza C (2015). Classifying GABAergic interneurons with semi-supervised projected model-based clustering. Artificial Intelligence in Medicine, 65(1), 49-59.
• Varando G, Lopez-Cruz PL, Nielsen TD, Larrañaga P, Bielza C (2015). Conditional density approximations with mixtures of polynomials. International Journal of Intelligent Systems, 30(3), 236–264.
• Varando G, Bielza C, Larrañaga P (2015). Decision boundary for discrete Bayesian network classifiers. Journal of Machine Learning Research, 16, 2725-2749.

2014

• Anton-Sanchez L, Bielza C, Merchán-Pérez A, Rodríguez JR, DeFelipe J, Larrañaga P (2014). Three-dimensional distribution of cortical synapses: A replicated point pattern-based analysis. Frontiers in Neuroanatomy, 8, 85.
• Bielza C, Larrañaga P (2014). Bayesian networks in neuroscience: A survey. Frontiers in Computational Neuroscience, 8, 131.
• Bielza C, Benavides-Piccione R, Lopez-Cruz PL, Larrañaga P, DeFelipe J (2014). Branching angles of pyramidal cell dendrites follow common geometrical design principles in different cortical areas. Scientific Reports, 4, 5909.
• Bielza C, Larrañaga P (2014). Discrete Bayesian network classifiers: A survey. ACM Computing Surveys, 47(1), 5.
• Guerra L, Bielza C, Robles V, Larrañaga P (2014). Semi-supervised projected model-based clustering. Data Mining and Knowledge Discovery, 28(4), 882-917.
• Ibañez A, Bielza C, Larrañaga P (2014). Cost-sensitive selective naive Bayes classifiers for predicting the increase of the h-index for scientific journals. Neurocomputing, 135(5), 45-52.
• Lopez-Cruz PL, Bielza C, Larrañaga P (2014). Learning mixtures of polynomials of multidimensional probability densities from data using B-spline interpolation. International Journal of Approximate Reasoning, 55(4), 989–1010.
• Lopez-Cruz PL, Larrañaga P, Bielza C (2014). Bayesian network modeling of the consensus between experts: An application to neuron classification. International Journal of Approximate Reasoning, 55(1), 3-22.
• Merchán-Pérez A, Rodríguez JR, González S, Robles V, DeFelipe J, Larrañaga P, Bielza C (2014). Three-dimensional spatial distribution of synapses in the neocortex: A dual-beam electron microscopy study. Cerebral Cortex, 24, 1579-1588.
• Mihaljević B, Bielza C, Benavides-Piccione R, DeFelipe J, Larrañaga P (2014). Multi-dimensional classification of GABAergic interneurons with Bayesian network-modeled label uncertainty, Frontiers in Computional Neuroscience, 8, 150.
• Morales J, Benavides-Piccione R, Dar M, Fernaud I, Rodríguez A, Anton-Sanchez L, Bielza C, Larrañaga P, DeFelipe J, Yuste R (2014). Random positions of dendritic spines in human cerebral cortex. Journal of Neuroscience, 34(30), 10078-10084.
• Read J, Bielza C, Larrañaga P (2014). Multi-dimensional classification with super-classes. IEEE Transactions on Knowledge and Data Engineering, 26(7), 1720-1733.
• Sucar E, Bielza C, Morales EF, Hernandez-Leal P, Zaragoza JH, Larrañaga P (2014). Multi-label classification with Bayesian network-based chain classifiers. Pattern Recognition Letters, 41, 14-22.

2013

• Armañanzas R, Bielza C, Chaudhuri KR, Martínez-Martín P, Larrañaga P (2013). Unveiling relevant non-motor Parkinson's disease severity symptoms using a machine learning approach. Artificial Intelligence in Medicine, 58(3), 195-202.
• Armañanzas R, Alonso-Nanclares L, DeFelipe J, Kastanauskaite A, de Sola RG, DeFelipe J, Bielza C, Larrañaga P (2013). Machine learning approach for the outcome prediction of temporal lobe epilepsy surgery. PLoS ONE, 8(4), e62819.
• Bielza C, Fernandez del Pozo JA, Larrañaga P (2013). Parameter control of genetic algorithms by learning and simulation of Bayesian networks. A case study for the optimal ordering of tables. Journal of Computer Science and Technology, 28(4), 720-731.
• Borchani H, Bielza C, Larrañaga P (2013). Predicting human immunodeficiency virus inhibitors using multi-dimensional Bayesian network classifiers. Artificial Intelligence in Medicine, 57(3), 219-229.
• DeFelipe J, Lopez-Cruz PL, Benavides-Piccione R, Bielza C, Larrañaga P, Ascoli GA (2013). New insights into the classification and nomenclature of cortical GABAergic interneurons. Nature Reviews Neuroscience, 14(3), 202-216. Highly cited paper.
• Flores JL, Inza I, Larrañaga P, Calvo B (2013). A new measure for gene expression biclustering based on non-parametric correlation. Computer Methods and Programs in Biomedicine, 113(3), 367-397.
• García-Torres M, Armañanzas R, Bielza C, Larrañaga P (2013). Comparison of metaheuristic strategies for peakbin selection in proteomic mass spectrometry data. Information Sciences, 222, 229-246.
• Ibañez A, Bielza C, Larrañaga P (2013). Relationship among research collaboration, number of documents and number of citations. A case study in Spanish computer science production in 2000-2009. Scientometrics, 95(2), 689-716.
• Ibañez A, Larrañaga P, Bielza C (2013). Cluster methods for assessing research performance: Exploring Spanish computer science. Scientometrics, 97, 571-600.
• Ibañez A, Bielza C, Larrañaga P (2013). Análisis de la actividad científica de las universidades públicas españolas en el área de las tecnologías informáticas. Revista Española de Documentación Científica, 36(1), e002.
• Karshenas H, Santana R, Bielza C, Larrañaga P (2013). Regularized continuous estimation of distribution algorithms. Applied Soft Computing, 13(5), 2412–2432.
• Karshenas H, Santana R, Bielza C, Larrañaga P (2013). Multi-objective estimation of distribution algorithm based on joint modeling of objectives and variables. IEEE Transactions on Evolutionary Computation, 18(4), 519-542.
• Larrañaga P, Karshenas H, Bielza C, Santana R (2013). A review on evolutionary algorithms in Bayesian network learning and inference tasks. Information Sciences, 233, 109-125.
• Morales D, Vives-Gilabert Y, Gómez-Ansón B, Bengoetxea E, Larrañaga P, Bielza C, Pagonabarraga J, Kulisevsky J, Corcuera-Solano I, Delfino M (2013). Predicting dementia development in Parkinson's disease using Bayesian network classifiers. Psychiatry Research: NeuroImaging, 213, 92-98.
• Santana R, Armañanzas R, Bielza C, Larrañaga P (2013). Network measures for information extraction in evolutionary algorithms. International Journal of Computational Intelligence Systems, 6(6), 1163-1188.
• Santana R, McGarry L, Bielza C, Larrañaga P, Yuste R (2013). Classification of neocortical interneurons using affinity propagation. Frontiers in Neural Circuits, 7, 185.
• Vidaurre D, Bielza C, Larrañaga P (2013). An L1-regularized naive Bayes-inspired classifier for discarding redundant and irrelevant predictors. International Journal on Artificial Intelligence Tools, 22(4), 1350019.
• Vidaurre D, Bielza C, Larrañaga P (2013). A survey on L1-regression. International Statistical Review, 81(3), 361-387.
• Vidaurre D, Bielza C, Larrañaga P (2013). Classification of neural signals from sparse autoregressive features. Neurocomputing, 111, 21-26.
• Vidaurre D, van Gerven M, Bielza C, Larrañaga P, Heskes T (2013). Bayesian sparse partial least squares. Neural Computation, 25(12), 3318–3339.
• Vidaurre D, Bielza C, Larrañaga P (2013). Sparse regularized local regression. Computational Statistics and Data Analysis, 62, 122-135.

2012

• Armañanzas R, Larrañaga P, Bielza C (2012). Ensemble transcript interaction networks: A case study on Alzheimer's disease. Computer Methods and Programs in Biomedicine, 108(1), 442-450.
• Borchani H, Bielza C, Martínez-Martín P, Larrañaga P (2012). Markov blanket-based approach for learning multi-dimensional Bayesian network classifiers: An application to predict the European Quality of Life-5Dimensions (EQ-5D) from the 39-item Parkinson's Disease Questionnaire (PDQ-39). Journal of Biomedical Informatics, 45, 1175-1184.
• Calvo B, Inza I, Larrañaga P, Lozano JA (2012). Wrapper positive Bayesian network classifiers. Knowledge and Information Systems, 33(3), 631-654.
• García-Bilbao A, Armañanzas R, Ispizua Z, Calvo B, Alonso-Varona A, Inza I, Larrañaga P, López-Vivanco G, Suárez-Merino B, Betanzos M (2012). Identification of a biomarker panel for colorectal cancer diagnosis. BMC Cancer, 12, 43.
• Guerra L, Robles V, Bielza C, Larrañaga P (2012). A comparison of clustering quality indices using outliers and noise. Intelligent Data Analysis, 16(4), 703-715.
• Larrañaga P, Karshenas H, Bielza C, Santana R (2012). A review on probabilistic graphical models in evolutionary computation. Journal of Heuristics, 18(5), 795-819.
• Santana R, Bielza C, Larrañaga P (2012). Regularized logistic regression and multi-objective variable selection for classifying MEG data. Biological Cybernetics, 106(6-7), 389-405.
• Santana R, Bielza C, Larrañaga P (2012). Conductance interaction identification by means of Boltzmann distribution and mutual information analysis in conductance-based neuron models. BMC Neuroscience, 13(1), P100.
• Vidaurre D, Bielza C, Larrañaga P (2012). Lazy lasso for local regression. Computational Statistics, 27(3), 531-550.
• Vidaurre D, Rodríguez EE, Bielza C, Larrañaga P, Rudomin P (2012). A new feature extraction method for signal classification applied to cat spinal cord signals. Journal of Neural Engineering, 9, 056009.

2011

• Armañanzas R, Saeys Y, Inza I, Garca-Torres M, Bielza C, van de Peer Y, Larrañaga P (2011). Peakbin selection in mass spectrometry data using a consensus approach with estimation of distribution algorithms. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 8(3), 760-774.
• Bengoetxea E, Larrañaga P, Bielza C, Fernández del Pozo JA (2011). Optimal row and column ordering to improve table interpretation using estimation of distribution algorithms. Journal of Heuristics, 17(5), 567-588.
• Bielza C, Robles V, Larrañaga P (2011). Regularized logistic regression without a penalty term: An application to cancer classification with microarray data. Expert Systems with Applications, 38, 5110-5118.
• Bielza C, Li G, Larrañaga P (2011). Multi-dimensional classification with Bayesian networks. International Journal of Approximate Reasoning, 52, 705-727.
• Borchani H, Larrañaga P, Bielza C (2011). Classifying evolving data streams with partially labeled data. Intelligent Data Analysis, 15(5), 655-670.
• Guerra L, McGarry L, Robles V, Bielza C, Larrañaga P, Yuste R (2011). Comparison between supervised and unsupervised classification of neuronal cell types: A case study. Developmental Neurobiology, 71(1), 71-82.
• Ibañez A, Larrañaga P, Bielza C (2011). Using Bayesian networks to discover relationships between bibliometric indices. A case study of computer science and artificial intelligence journals. Scientometrics, 89(2), 523-551.
• Larrañaga P, Moral S (2011). Probabilistic graphical models in artificial intelligence. Applied Soft Computing, 11(2), 1511-1528.
• López-Cruz PL, Bielza C, Larrañaga P, Benavides-Piccione R DeFelipe, J (2011). Models and simulation of 3D neuronal dendritic trees using Bayesian networks. Neuroinformatics, 9, 347-369.
• Santana R, Bielza C, Larrañaga P (2011). Optimizing brain networks topologies using multi-objective evolutionary computation. Neuroinformatics, 9(1), 3-19.
• Vidaurre D, Bielza C, Larrañaga P (2011). On nonlinearity in neural encoding models applied to the primary visual cortex. Network: Computation in Neural Systems, 22(1-4), 97-125.
• Vidaurre D, Bielza C, Larrañaga P (2011). Forward stagewise naive Bayes. Progress in Artificial Intelligence, 1, 57-69.

2010

• Bielza C, Fernandez del Pozo JA, Larrañaga P, Bengoetxea E (2010). Multidimensional statistical analysis of the parameterization of a genetic algorithm for the optimal ordering of tables. Expert Systems with Applications, 37, 804-815.
• Cuesta I, Bielza C, Cuenca-Estrella M, Larrañaga P, Rodriguez-Tudela JL (2010). Evaluation by data mining techniques of fluconazole breakpoints established by the Clinical and Laboratory Standards Institute (CLSI) and comparison with those of the European Committee on Antimicrobial Susceptibility Testing (EUCAST). Antimicrobial Agents and Chemotherapy, 54(4), 1541-1546.
• Santana R, Larrañaga P, Lozano JA (2010). Learning factorizations in estimation of distribution algorithms using affinity propagation. Evolutionary Computation, 18(4), 515-546.
• Santana R, Bielza C, Larrañaga P, Lozano JA, Echegoyen C, Mendiburu A, Armañanzas R, Shakya S (2010). MATEDA-2.0: A Matlab package for the implementation and analysis of estimation of distribution algorithms. Journal of Statistical Software, 35(7), 1-30.
• Vidaurre D, Bielza C, Larrañaga P (2010). Learning an L1-regularized Gaussian Bayesian network in the equivalence class space. IEEE Transactions on Systems, Man and Cybernetics, Part B, 40(5), 1231-1242.

2009

• Armañanzas R, Calvo B, Inza I, López-Hoyos M, Martínez-Taboada V, Ucar E, Bernales I, Fullaondo A, Larrañaga P, Zubiaga AM (2009). Microarray analysis of autoimmune diseases by machine learning procedures. IEEE Transactions on Information Technology in Biomedicine, 13(3), 341-350.
• Bielza C, Robles V, Larrañaga P (2009). Estimation of distribution algorithms as logistic regression regularizers of microarray classifiers. Methods of Information in Medicine, 48(3), 236-241.
• Cuesta I, Bielza C, Larrañaga P, Cuenca-Estrella M, Laguna F, Rodríguez-Pardo D, Almirante B, Pahissa A, Rodriguez-Tudela JL (2009). Data mining validation of EUCAST fluconazole breakpoints established by the European Committee on Antimicrobial Susceptibility Testing. Antimicrobial Agents and Chemotherapy, 53(7), 2949-2954.
• Ibañez A, Larrañaga P, Bielza C (2009). Predicting citation count of bioinformatics papers within four years of publication. Bioinformatics, 25(24), 3303-3309.
• Pérez A, Larrañaga P, Inza I (2009). Bayesian classifiers based on kernel estimation: Flexible classifiers. International Journal of Approximate Reasoning, 50(2), 341-362.
• Romero T, Larrañaga P (2009). Triangulation of Bayesian networks with recursive estimation of distribution algorithms. International Journal of Approximate Reasoning, 50(3), 472-484.

2008

• Armañanzas R, Inza I, Larrañaga P (2008). Detecting reliable gene interactions by a hierarchy of Bayesian networks classifiers. Computer Methods and Programs in Biomedicine, 91, 110-121.
• Furney SJ, Calvo B, Larrañaga P, Lozano JA, López-Bigas N (2008). Prioritization of candidate cancer genes-an aid to oncogenomic studies. Nucleic Acids Research, 1-9.
• Morales D, Bengoetxea E, Larrañaga P (2008). Selection of human embryos for transfer by Bayesian classifiers. Computers in Biology and Medicine, 38, 1177-1186.
• Morales D, Bengoetxea E, Larrañaga P, Garca M, Franco-Iriarte Y, Fresnada M, Merino M (2008). Bayesian classification for the selection of in-vitro human embryos using morphological and clinical data. Computer Methods and Programs in Biomedicine, 90, 104-116.
• Robles V, Bielza C, Larrañaga P, González S, Ohno-Machado L (2008). Optimizing logistic regression coefficients for discrimination and calibration using estimation of distribution algorithms. TOP, 16, 345-366.
• Santafé G, Lozano JA, Larrañaga P (2008). Inference of population structure using genetic markers and a Bayesian model averaging approach for clustering. Journal of Computational Biology, 15(2), 207-220.
• Santana R, Lozano JA, Larrañaga P (2008). Protein folding in simplified models with estimation of distribution algorithms. IEEE Transactions on Evolutionary Computation, 12(4), 418-438.
• Santana R, Larrañaga P, Lozano JA (2008). Combining variable neighborhood search and estimation of distribution algorithms. Journal of Heuristics, 14, 519-547.
• Zipritia I, Elorriaga J, Arruarte A, Larrañaga P, Armañanzas R (2008). What is behind a summary evaluation decision?. Behavior Research Methods, 2(40), 597-612.

2007

• Calvo B, López-Bigas N, Furney SJ, Larrañaga P, Lozano JA (2007). A partially supervised approach to dominant and recessive human disease gene prediction, Computer Methods and Programs in Biomedicine, 85, 3, 229-237.
• Calvo B, Lozano JA, Larrañaga P (2007). Learning bayesian classifiers from positive and unlabeled examples, Pattern Recognition Letters, 28, 16, 2375-2384.
• Flores JL, Inza I, Larrañaga P (2007). Wrapper discretization by means of estimation of distribution algorithms, Intelligent Data Analysis Journal, 11, 5, 525-546.
• Miquelez T, Bengoetxea E, Mendiburu A, Larrañaga P (2007). Combining Bayesian classifiers and estimation of distribution algorithms for optimization in continuous domains, Connection Science, 19, 4, 297-319, 2007.
• Saeys Y, Inza I, Larrañaga P (2007) A review of feature selection techniques in bioinformatics, Bioinformatics, 23, 19, 2507-2517.
• Santana R, Larrañaga P, Lozano JA (2007). Side chain placement using estimation of distribution algorithms, Artificial Intelligence in Medicine, 39, 1, 49-63.

2006

• Larrañaga P, Calvo B, Santana R, Bielza C, Galdiano J, Inza I, Lozano JA, Armañanzas R, Santafé, G Pérez A (2006). Machine learning in bioinformatics, Briefings in Bioinformatics, 17, 1, 86-112.
• Pérez A, Larrañaga P, Inza I (2006). Supervised classification with conditional Gaussian networks: Increasing the structure complexity from naive Bayes, International Journal of Approximate Reasoning, 43, 1-25.
• Santafé, G Lozano, JA Larrañaga P (2006). Bayesian model averaging of naive Bayes for clustering, IEEE Transactions on Systems, Man, Cybernetics, 36, 5, 1149-1161.

2005

• Blanco R, Inza I, Merino M, Quiroga J, Larrañaga P (2005). Feature selection in Bayesian classifiers for the prognosis of survival of cirrhotic patients treated with TIPS, Journal of Biomedical Informatics, 38, 376–388.
• Larrañaga P, Lozano JA (2005) Special issue on estimation of distribution algorithms, Evolutionary Computation, 13, 1, v-vi.
• Larrañaga P, Lozano JA, Peña JM, Inza I (2005). Special issue on probabilistic graphical models in classification, Machine Learning, 59, 211-212.
• Peña JM, Lozano JA, Larrañaga P (2005). Globally multimodal problem optimization via an estimation of distribution algorithm based on unsupervised learning of Bayesian networks, Evolutionary Computation, 43-66.
• Roberto C, Bengoetxea E, Bloch I, Larrañaga P (2005). Inexact graph matching for model-based recognition: Evaluation and comparison of optimization algorithms, Pattern Recognition, 38, 2099–2113.

2004

• Blanco R, Larrañaga P, Inza I, Sierra B (2004). Gene selection for cancer classification using wrapper approaches, International Journal of Pattern Recognition and Artificial Intelligence, 18, 8, 1373-1390.
• Inza I, Larrañaga P, Blanco R, Cerrolaza AJ (2004) Filter versus wrapper gene selection approaches in DNA microarray domains, Artificial Intelligence in Medicine, 31, 91-103.
• Larrañaga P, Menasalvas E, Peña JM, Robles V (2004) Special issue in data mining in genomics and proteomics, Artificial Intelligence in Medicine, 31, iii-iv.
• Miquelez T, Bengoetxea E, Larrañaga P (2004). Evolutionary computation based on Bayesian classifiers, International Journal of Applied Mathematics and Computer Science, 14, 3, 101–115.
• Peña JM, Lozano JA, Larrañaga P (2004). Unsupersived learning of Bayesian networks via estimation of distribution algorithms: an application to gene expression data clustering, International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 12, 63-82.
• Robles V, Larrañaga P, Peña JM, Menasalvas E, Pérez MS, Herves V (2004). Bayesian networks as consensed voting system in the construction of a multi–classifier for protein secondary structure prediction, Artificial Intelligence in Medicine, 31, 117–136.
• Romero T, Larrañaga P, Sierra B (2004). Learning Bayesian networks in the space of orderings with estimation of distribution algorithms, International Journal of Pattern Recognition and Artificial Intelligence, 18, 4, 607–625.

2002

• Bengoetxea E, Larrañaga P, Bloch I, Perchant A, Boeres C (2002). Inexact graph matching by means of estimation of distribution algorithms, Pattern Recognition, 35, 12, 2867–2880.
• González C, Lozano JA, Larrañaga P (2002). Mathematical modelling of UMDAc algorithm with tournament selection. Behaviour on linear and quadratic functions, International Journal of Approximate Reasoning, 31, 313–340.
• Larrañaga P, Lozano JA (2002). Special issue on synergies between evolutionary computation and probabilistic graphical models, International Journal of Approximate Reasoning, 31, 155–156.
• Peña JM, Lozano JA, Larrañaga P (2002). Learning recursive Bayesian multinets for clustering by means of constructive induction, Machine Learning, 47, 63-89.

2001

• Inza I, Larrañaga P, Sierra B (2001). Feature subset selection by Bayesian networks: a comparison with genetic and sequential algorithms, International Journal of Approximate Reasoning, 27, 143–164.
• Inza I, Merino M, Larrañaga P, Quiroga J, Sierra B, Girala M (2001). Feature subset selection by genetic algorithms and estimation of distribution algorithms. A case study in the survival of cirrhotic patients treated with TIPS, Artificial Intelligence in Medicine, 23, 2, 187–205.
• Peña JM, Lozano JA, Larrañaga P (2001). Performance evaluation of compromise conditional Gaussian networks for data clustering, International Journal of Approximate Reasoning, 28, 23-50.
• Peña JM, Lozano JA, Larrañaga P, Inza I (2001). Dimensionality reduction in unsupervised learning of conditional Gaussian networks, IEEE Transactions on Pattern Analysis and Machine Intelligence, 23, 6, 590–603, 2001.
• Sierra B, Serrano N, Larrañaga P, Plasencia EJ, Inza I, Jiménez JJ, Revuelta P, Mora ML (2001). Using Bayesian networks in the construction of a bi-level multi–classifier. A case study using intensive care unit patients data, Artificial Intelligence in Medicine, 22, 233-248.

2000

• Inza I, Larrañaga P, Etxeberria R, Sierra B (2000). Feature subset selection by Bayesian network–based optimization, Artificial Intelligence, 123, 157-184, 2000.
• Peña JM, Lozano JA, Larrañaga P (2000). An improved Bayesian structural EM algorithm for learning Bayesian networks for clustering, Pattern Recognition Letters, 21, 8, 779-786, 2000.

1999

• Inza I, Larrañaga P, Sierra B, Etxeberria R, Lozano JA, Peña JM (1999). Representing the behaviour of supervised classification learning algorithms by Bayesian networks, Pattern Recognition Letters, 20, 11-13, 1201-1209.
• Larrañaga P, Kuijpers CMH, Murga RH, Inza I, Dizdarevich S (1999). Genetic algorithms for the travelling salesman problem: A review of representations and operators, Artificial Intelligence Review, 13, 129-170.
• Lozano JA, Larrañaga P (1999) Applying genetic algorithms to search for the best hierarchical clustering of a dataset, Pattern Recognition Letters, 20, 9, 911-918.
• Lozano JA, Larrañaga P, Graña M, Albizuri FX (1999). Genetic algorithms: Bridging the convergence gap, Theoretical Computer Science, 229, 11-22.
• Peña JM, Lozano JA, Larrañaga P (1999). Learning Bayesian networks for clustering by means of constructive induction, Pattern Recognition Letters, 20, 11-13, 1219-1230.
• Peña JM, Lozano JA, Larrañaga P (1999). An empirical comparison of four initialization methods for the k-means algorithm, Pattern Recognition Letters, 20, 1027-1040.

1998

• Sierra B, Larrañaga P (1998) Predicting the survival in malignant skin melanoma using Bayesian networks automatically induced by genetic algorithms. An empirical comparison between different approaches, Artificial Intelligence in Medicine, 14, 1-2, 215-230.

1997

• Albizuri X, D’Anjou A, Graña M, Larrañaga P (1997). Structure of the high-order Boltzman machine from independence maps, IEEE Transactions on Neural Networks, 8, 6, 1351-1358.
• Etxeberria R, Larrañaga P, Pikaza JM (1997). Analysis of the behaviour of genetic algorithms when learning Bayesian network structure from data, Pattern Recognition Letters, 18, 11-13, 1269-1273.
• Larrañaga P, Kuijpers CMH, Poza M, Murga RH (1997). Decomposing Bayesian networks: Triangulation of the moral graph with genetic algorithms, Statistics and Computing, 7, 1, 19-34.

1996

• Larrañaga P, Poza M, Yurramendi Y, Murga RH, Kuijpers CMH (1996). Structure learning of Bayesian networks by genetic algorithms: A performance analysis of control parameters, IEEE Transactions on Pattern Analysis and Machine Intelligence, 18, 9, 912-926.
• Larrañaga P, Kuijpers CMH, Murga RH, Yurramendi Y (1996). Learning Bayesian network structures by searching for the best ordering with genetic algorithms, IEEE Transactions on System, Man and Cybernetics. Part A: Systems and Humans, 26, 4, 487-493.

1988

• Emparanza JI, Aldámiz-Echevarria L, Pérez-Yarza EG, Larrañaga P, Jimenez JL, Labiano M, Ozcoidi I (1988). Prognostic score in acute meningococcemia, Critical Care Medicine, 16, 2, 168-169.