• C. Villa-Blanco, C. Bielza, P. Larrañaga (2022). Feature subset selection for data and feature streams: A review. Artificial Intelligence Review, in press.



  • Córdoba-Sánchez, I., C. Bielza, P. Larrañaga, and G. Varando, “Sparse Cholesky Covariance Parametrization for Recovering Latent Structure in Ordered Data”, IEEE Access, vol. 8, pp. 154614 – 154624, 2020. 
  • Gil-Begue, S., C. Bielza, and P. Larrañaga, “Multi-dimensional Bayesian network classifiers: A survey”, Artificial Intelligence Review, vol. accepted, 2020. BibTex
  • Michiels, M., P. Larrañaga, and C. Bielza, “BayeSuites: An open web framework for massive Bayesian networks focused on neuroscience”, Neurocomputing, vol. 428, pp. 166 – 181, 2020. BibTex



  • Anton-Sanchez, L., F..Effenberger, C. Bielza, P. Larrañaga, and H.. Cuntz, “A regularity index for dendrites – local statistics of a neuron’s input space”, PLoS Computational Biology, vol. 14, 11, e1006593, 2018. BibTex


  • Anton-Sanchez, L., P. Larrañaga, R. Benavides-Piccione, I. Fernaud, J. DeFelipe, and C. Bielza, “Three-dimensional spatial modeling of spines along dendritic networks in human cortical pyramidal neurons”, PLoS ONE 12(6): e0180400, 2017. BibTex
  • Anton-Sanchez, L., C. Bielza, and P. Larrañaga, “Network Design through Forests with Degree- and Role-constrained Minimum Spanning Trees”, Journal of Heuristics, vol. 23, issue 1, pp. 31-51, 2017. BibTex
  • Mu, J., K. R. Chaudhuri, C. Bielza, J. de Pedro-Cuesta, P. Larrañaga, and P. Martínez-Martín, “Parkinson’s Disease Subtypes Identified from Cluster Analysis of Motor and Non-motor Symptoms”, Frontiers in Aging Neuroscience, vol. 9, pp. Article 301, 2017. BibTex
  • Rodriguez-Lujan, L., P. Larrañaga, and C. Bielza, “Frobenius norm regularization for the multivariate von Mises distribution”, International Journal of Intelligent Systems, vol. 32, issue 2, pp. 153–176, 2017. BibTex



  • Krallinger, M., F. Leitner, and O. Rabal, “CHEMDNER: The drugs and chemical names extraction challenge”, Journal of Chemoinformatics, vol. T, no. Suppl 1, pp. S1, 2015. BibTex
  • Larrañaga, A., C. Bielza, P. Pongrácz, T. Faragó, P. Bálint, and P. Larrañaga, “Comparing supervised learning methods for classifying sex, age, context and individual Mudi dogs from barking”, Animal Cognition, vol. 18, no. 2, pp. 405-421, 2015. BibTex
  • Lopez-Cruz, P. L., C. Bielza, and P. Larrañaga, “Directional naive Bayes classifiers”, Pattern Analysis and Applications, vol. 18, pp. 225-246, 2015. BibTex
  • Luengo-Sanchez, S., C. Bielza, R. Benavides-Piccione, I. Fernaud-Espinosa, J. DeFelipe, and P. Larrañaga, “A univocal definition of the neuronal soma morphology using Gaussian mixture models”, Frontiers in Neuroanatomy, vol. 9, issue 137, 2015. BibTex
  • Mihaljevic, B., R. Benavides-Piccione, C. Bielza, J. DeFelipe, and P. Larrañaga, “Bayesian network classifiers for categorizing cortical GABAergic interneurons”, Neuroinformatics, vol. 13, no. 2, pp. 192–208, April, 2015. BibTex


  • Merchán-Pérez, A., R. Rodríguez, S. González, V. Robles, J. DeFelipe, P. Larrañaga, and C. Bielza, “Three-dimensional spatial distribution of synapses in the neocortex: A dual-beam electron microscopy study”, Cerebral Cortex, vol. 24, pp. 1579-1588, 2014. 


  • Vidaurre, D., C. Bielza, and P. Larrañaga, A survey on L1-regression“, International Statistical Review, vol. 81, no. 3, pp. 361-387, 2013. 



  • Vidaurre, D., C. Bielza, and P. Larrañaga, “Forward Stagewise Naive Bayes”, Progress in Artificial Intelligence, vol. In press, 2011. 



  • Armañanzas, R., B. Calvo, I. Inza, M. López-Hoyos, V. Martínez-Taboada, E. Ucar, I. Bernales, A. Fullaondo, P. Larrañaga, and A. M. Zubiaga, “Microarray analysis of autoimmune diseases by machine learning procedures”, IEEE Transactions on Information Technology in Biomedicine, vol. 13, no. 3, pp. 341-350, 2009. 
  • Ibañez, A., P. Larrañaga, and C. Bielza, “Predicting Citation Count of Bioinformatics Papers within Four Years of Publication”, Bioinformatics, vol. 25, no. 24, pp. 3303-3309, 2009. 


  • Correa, M., C. Bielza, M. J. Ramírez, and J. R. Alique, “A Bayesian Network Model for Surface Roughness Prediction in the Machining Process”, International Journal of Systems Science, vol. 39, no. 12, pp. 1181-1192, 2008. 
  • Morales, D., E. Bengoetxea, P. Larrañaga, M. Garca, Y. Franco-Iriarte, M. Fresnada, and M. Merino, “Bayesian classification for the selection of in-vitro human embryos using morphological and clinical data”, Computer Methods and Programs in Biomedicine, no. 90, pp. 104-116, 2008. 
  • Robles, V., C. Bielza, P. Larrañaga, S. González, and L. Ohno-Machado, “Optimizing Logistic Regression Coefficients for Discrimination and Calibration Using Estimation of Distribution Algorithms”, TOP, vol. 16, pp. 345-366, 2008. 
  • Santafé, G., J. A. Lozano, and P. Larrañaga, “Inference of population structure using genetic markers and a Bayesian model averaging approach for clustering”, Journal of Computational Biology, vol. 15, no. 2, pp. 207-220, 2008. 


  • Santana, R., J. A. Lozano, and P. Larrañaga, “Protein folding in simplified models with estimation of distribution algorithms”, IEEE Transactions on Evolutionary Computation, vol. 12, no. 4, pp. 418-438, 2008. 
  • Santana, R., P. Larrañaga, and J. A. Lozano, “Combining variable neighborhood search and estimation of distribution algorithms”, Journal of Heuristics, vol. 14, pp. 519-547, 2008. 


  • Calvo, B., J. A. Lozano, and P. Larrañaga, “Learning Bayesian classifiers from positive and unlabeled examples”, Pattern Recognition Letters, vol. 28, no. 16, pp. 2375-2384, 2007. 
  • Flores, J. L., I. Inza, and P. Larrañaga, “Wrapper discretization by means of estimation of distribution algorithms”, Intelligent Data Analysis Journal, vol. 11, no. 5, pp. 525-546, 2007. 
  • Gómez, M., C. Bielza, J. A. Fernandez del Pozo, and S. Ríos-Insua, “A Graphical Decision-Theoretic Model for Neonatal Jaundice”, Medical Decision Making, vol. 27, no. 3, pp. 250-265, 2007. 
  • Saeys, Y., I. Inza, and P. Larrañaga, “A review of feature selection techniques in bioinformatics”, Bioinformatics (most highly cited), vol. 23, no. 19, pp. 2507-2517, 2007. 


  • Larrañaga, P., B. Calvo, R. Santana, C. Bielza, J. Galdiano, I. Inza, J. A. Lozano, R. Armañanzas, G. Santafé, and A. Pérez, “Machine Learning in Bioinformatics”, Briefings in Bioinformatics, vol. 17, no. 1, pp. 86-112, 2006. 
  • Pérez, A., P. Larrañaga, and I. Inza, “Supervised classification with conditional Gaussian networks: Increasing the structure complexity from naive Bayes”, International Journal of Approximate Reasoning, vol. 43, pp. 1-25, 2006. 


  • Larrañaga, P., and J. A. Lozano, “Special issue on estimation of distribution algorithms”, Evolutionary Computation, vol. 13, no. 1, pp. v-vi, 2005. 
  • Martín, J., C. Bielza, and D. Ríos Insua, “Approximating Nondominated Sets in Continuous Multiobjective Optimization Problems”, Naval Research Logistics, vol. 52, no. 5, pp. 469-480, 2005. 


  • Blanco, R., P. Larrañaga, I. Inza, and B. Sierra, “Gene selection for cancer classification using wrapper approaches”, International Journal of Pattern Recognition and Artificial Intelligence, vol. 18, no. 8, pp. 1373-1390, 2004. 
  • Gómez, M., and C. Bielza, “Node Deletion Sequences in Influence Diagrams Using Genetic Algorithms”, Statistics {&} Computing, vol. 14, no. 3, pp. 181-198, 2004. 
  • Miquelez, T., E. Bengoetxea, and P. Larrañaga, “Evolutionary computation based on Bayesian classifiers”, International Journal of Applied Mathematics and Computer Science, vol. 14, no. 3, pp. 101–115, 2004. 
  • Owodunni, O. O., J. Diaz-Rozo, and S. Hinduja, “Development and Evaluation of a Low-Cost Computer Controlled Reconfigurable Rapid Tool”, Computer-Aided Design and Applications, vol. 1, issue 1-4, pp. 101–108, 2004. 
  • Romero, T., P. Larrañaga, and B. Sierra, “Learning Bayesian networks in the space of orderings with estimation of distribution algorithms”, International Journal of Pattern Recognition and Artificial Intelligence, vol. 18, no. 4, pp. 607–625, 2004. 


  • Ballestero, E., J. M. Antón, and C. Bielza, “Compromise-Based Approach to Road Project Selection in Madrid Metropolitan Area”, Journal of the Operations Research Society of Japan, vol. 46, no. 1, pp. 99-122, 2003. 
  • Blanco, R., I. Inza, and P. Larrañaga, “Learning Bayesian networks in the space of structures by estimation of distribution algorithms”, International Journal of Intelligent Systems, vol. 18, pp. 205-220, 2003. 


  • González, C., J. A. Lozano, and P. Larrañaga, “Modelado matemático del algoritmo UMDAc con selección por torneo aplicado a funciones lineales”, Primer Congreso Español de Algoritmos Evolutivos y Bioinspirados, pp. 437–444, 2002. 
  • Inza, I., B. Sierra, R. Blanco, and P. Larrañaga, “Gene selection by sequential search wrapper approaches in microarray cancer class prediction”, Journal of Intelligent and Fuzzy Systems, vol. 12, no. 1, pp. 25-33, 2002. 
  • Peña, J. M., J. A. Lozano, and P. Larrañaga, “Learning recursive Bayesian multinets for clustering by means of constructive induction”, Machine Learning, vol. 47, pp. 63-89, 2002. 


  • Peña, J. M., J. A. Lozano, P. Larrañaga, and I. Inza, “Dimensionality reduction in unsupervised learning of conditional Gaussian networks”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 23, no. 6, pp. 590–603, 2001. 


  • González, C., J. A. Lozano, and P. Larrañaga, “Analyzing the population based incremental learning algorithm by means of discrete dynamical systems”, Complex Systems, vol. 12, no. 4, pp. 465–479, 2000. 


  • Bielza, C., and P. P. Shenoy, “A Comparison of Graphical Techniques for Asymmetric Decision Problems”, Management Science, vol. 45, no. 11, 1999. 
  • Bielza, C., P. Müller, and D. Ríos Insua, “Decision Analysis by Augmented Probability Simulation”, Management Science, vol. 45, no. 7, pp. 995-1007, 1999. 
  • Larrañaga, P., C. M. H. Kuijpers, R. H. Murga, I. Inza, and S. Dizdarevich, “Genetic algorithms for the travelling salesman problem: A review of representations and operators”, Artificial Intelligence Review, vol. 13, pp. 129-170, 1999. 



  • Larrañaga, P., C. M. H. Kuijpers, M. Poza, and R. H. Murga, “Decomposing Bayesian networks: triangulation of the moral graph with genetic algorithms”, Statistics and Computing, vol. 7, no. 1, pp. 19-34, 1997. 


  • Larrañaga, P., M. Poza, Y. Yurramendi, R. H. Murga, and C. M. H. Kuijpers, “Structure learning of Bayesian networks by genetic algorithms: A performance analysis of control parameters”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 18, no. 9, pp. 912-926, 1996.
  • Larrañaga, P., C. M. H. Kuijpers, R. H. Murga, and Y. Yurramendi, “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, vol. 26, no. 4, pp. 487-493, 1996.


  • Emparanza, J. I., L. Aldámiz-Echevarria, E. G. Pérez-Yarza, P. Larrañaga, J. L. Jimenez, M. Labiano, and I. Ozcoidi, Prognostic score in acute meningococcemia“, Critical Care Medicine, vol. 16, no. 2, pp. 168-169, 1988.