Concha Bielza

Concha Bielza received the M.S. degree in Mathematics from Universidad Complutense de Madrid, Madrid, Spain, in 1989 and the Ph.D. degree in Computer Science from Universidad Politécnica de Madrid, Madrid, in 1996 (extraordinary doctorate award). She is currently (since 2010) a Full Professor of Statistics and Operations Research with the Departamento de Inteligencia Artificial, Universidad Politécnica de Madrid.

Her research interests are primarily in the areas of probabilistic graphical models, decision analysis, metaheuristics for optimization, data mining, classification models, and real applications, like biomedicine, bioinformatics, neuroscience, industry and sport analytics.

She has published more than 160 papers in impact factor journals and has supervised 23 PhD theses. She was awarded the 2014 UPM Research Prize, the 2020 Research Award for Significant Contribution in the Field of Machine Learning (India) and the 2024 National Statistics Prize by the National Institute of Statistics (Spain). She is ELLIS Fellow since 2023 and Fellow of the Asia-Pacific Artificial Intelligence Association since 2024. She is Member of the Scientific Advisory Board of NorwAI (Norwegian Research Center for AI Innovation), since 2021.

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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

• Bielza C, Moral S, Salmerón A (2015). Recent advances in probabilistic graphical models. International Journal of Intelligent Systems, 30, 207-208.
• Bielza C, Gama J, Jorge A, Žliobaité, I (2015). Guest editors introduction: Special issue of the ECMLPKDD 2015 journal track. Data Mining and Knowledge Discovery, 29, 1113–1115.
• Bielza C, Gama J, Jorge A, Žliobaité, I (2015). Guest editors introduction: Special issue of the ECMLPKDD 2015 journal track. Machine Learning, 100, 157–159.
• 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.
• 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.
• Dueñas M, Santos M, Aranda FJ, Bielza C, Martínez-Cruz AB, García-Escudero R (2012). Mouse p53-deficient cancer models as platforms for obtaining genomic predictors of human cancer clinical outcomes. PLoS ONE, 7(8), e42494.
• 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.
• Maojo V, Garcia-Remesal M, Bielza C, Crespo J, Perez-Rey D, Kulikowski C (2012). Biomedical informatics publications: A global perspective. Part II: Journals. Methods of Information in Medicine, 51(2), 131-137.
• Maojo V, Garcia-Remesal M, Bielza C, Crespo J, Perez-Rey D, Kulikowski C (2012). Biomedical informatics publications: A global perspective. Part I: Conferences. Methods of Information in Medicine, 51(1), 82-90.
• 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, Gómez M, Shenoy P (2011). A review of representation issues and modelling challenges with influence diagrams. Omega -The International Journal of Management Science, 39, 227-241.
• 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.
• Fernández del Pozo JA, Bielza C (2011). Dealing with complex queries in decision support systems. Data Knowledge Engineering, 70, 167-181.
• 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.
• 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.
• Bielza C, Gómez M, Shenoy P (2010). Modelling challenges with influence diagrams: Constructing probability and utility models. Decision Support Systems, 49, 354-364.
• 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, 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

• 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.
• Correa M, Bielza C, Pamies-Teixeira J (2009). Comparison of Bayesian networks and artificial neural networks for quality detection in a machining process. Expert Systems with Applications, 36, 7270-7279.
• 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.

2008

• Bielza C, Fernández del Pozo JA, Lucas P (2008). Explaining clinical decisions by extracting regularity patterns. Decision Support Systems, 44, 397-408.
• Correa M, Bielza C, Ramírez MJ, Alique JR (2008). A Bayesian network model for surface roughness prediction in the machining process. International Journal of Systems Science, 39(12), 1181-1192.
• 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.

2007

• Gómez M, Bielza C, Fernandez del Pozo JA, Ríos-Insua S (2007). A graphical decision-theoretic model for neonatal jaundice, Medical Decision Making, 27, 3, 250-265.

2006

• Ballestero E, Bielza C, Pla-Santamaría D (2006). A decision approach to competitive electronic sealed-bid auctions for land, Journal of the Operational Research Society, 57, 1126-1133.
• 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.

2005

• Fernandez del Pozo JA, Bielza C, Gómez M (2005). A list-based compact representation for large decision tables management, European Journal of Operational Research, 160, 3, 638-662.
• Martín J, Bielza C, Ríos Insua D (2005). Approximating nondominated sets in continuous multiobjective optimization problems, Naval Research Logistics, 52, 5, 469-480.

2004

• Gómez M, Bielza C (2004) Node deletion sequences in influence diagrams using genetic algorithms, Statistics & Computing, 14, 3, 181-198, 2004.

2003

• Ballestero E, Antón JM, Bielza C (2003). Compromise-based approach to road project selection in madrid metropolitan area, Journal of the Operations Research Society of Japan, 46, 1, 99-122.
• Bielza C, Barreiro P, Rodríguez-Galiano MI, Martín J (2003). Logistic regression for simulating damage occurrence on a fruit grading line, Computers Electronics in Agriculture, 38, 2, 95-113.

2000

• Bielza C, Gómez M, Ríos-Insua S, Fernández del Pozo JA (2000). Structural, elicitation and computational issues faced when solving complex decision making problems with influence diagrams, Computers and Operations Research, 27, 7-8, 725-740, 2000.

1999

• Bielza C, Shenoy PP (1999). A Comparison of graphical techniques for asymmetric decision problems, Management Science, 45, 11.
• Bielza C, Müller P, Ríos Insua D (1999). Decision analysis by augmented probability simulation, Management Science, 45, 7, 995-1007.

1998

• Vidakovic B, Bielza C (1998) On time-dependent wavelet denoising, IEEE Transactions on Signal Processing, 46, 9, 2549-2554.