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=h\:#8X@"1Arial1Arial1Arial1Arial1 Arial"$"#,##0_);\("$"#,##0\)!"$"#,##0_);[Red]\("$"#,##0\)""$"#,##0.00_);\("$"#,##0.00\)'""$"#,##0.00_);[Red]\("$"#,##0.00\)7*2_("$"* #,##0_);_("$"* \(#,##0\);_("$"* ""_);_(@_).))_(* #,##0_);_(* \(#,##0\);_(* ""_);_(@_)?,:_("$"* #,##0.00_);_("$"* \(#,##0.00\);_("$"* ""??_);_(@_)6+1_(* #,##0.00_);_(* \(#,##0.00\);_(* ""??_);_(@_) + ) , * `3Proyectos de I+D+i%Estancias y sabticos recogid%Tesis DoctoralesArtculos en revistasCaptulos de libros%Conferencias invitadas en con%=Cursos, seminarios y tutorial%fInformes para las AAPP o sus LibrosOtras PublicacionesPonencia en CongresosCreacin de empresasKnowHowPatentes_Registros de SoftwarelVariedades vegetalesy
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TtuloDescripcin
InternacionalTipo de proyectoEntidad financiadoraNacionalidad EntidadTamao de la entidadFecha concesin
ParticipantesOtros ParticipantesCANLISIS DE BIOINFORMTICO DE QPCR PARA EL PROYECTO CRCBIOMARKERSInformes de anlisis numricos centrados en el preprocesado de datos de qPCR y la validacin de los modelos clasificatorios generados a partir de ellos0Proyectos y convenios de financiacin privadaUniversidad del Pas VascoESPAAPequea Empresa (1150)
27/03/2012&Director: Ruben Armaanzas Arnedillo//BCONVENIO DE COLABORACION PARA LA EJECUCION DEL PROYECTO BLUE BRAINA falta de descripcin<Proyectos y convenios en convocatorias pblicas competitivas'Ministerio de Economa y CompetitividadDesconocidoRParticipante: Pedro Maria Larraaga Mugica//Director: Javier De Felipe Oroquieta//yDEFINICIN DE UN SISTEMA DE CLASIFICACIN DE PROBLEMAS MATEMTICOS EN EL ENTORNO ATM Y LA APLICACIN DE NUEVAS SOLUCIONESCRIDA proporcionar la informacin necesaria y soporte tcnico requerido para que la UPM desarrolle las siguientes actividades en el marco de este Convenio Especfico:
? Analizar en profundidad los problemas matemticos detectados por CRIDA y las soluciones aplicadas en proyectos previos como IRCO, ABACO, ETLM, ATMAN, PERSEO, GESTUR, VALTUR, SALOMON, ATMAN, LEONARDO, ATON, MASDATA, GENES, SIMCO o PITOT, entre otros.
? Identificar tcnicas de resolucin de los problemas planteadas, justificando en detalle o bien demostrando su aplicabilidad.
? Establecer y justificar, siguiendo los principios de la taxonoma, un sistema de clasificacin de los problemas matemticos detectados, teniendo en cuenta entre otros factores las tcnicas de resolucin planteadas.
? Consensuar un sistema de clasificacin comn entre la UPM, CRIDA y la Universidad Autnoma de Barcelona, que forma parte del desarrollo de estas actividades al igual que la UPM.
? Analizar soluciones innovadoras aplicables a la clasificacin de problemas consensuada, planteando sus principales fortalezas y debilidades.
? Si las dos Partes lo acuerdan, consolidar los resultados obtenidos en un documento publicable o presentable a congresos o conferencias.
<Proyectos y convenios de financiacin pblica no competitivaCRIDADirector: Alfonso Mateos Caballero//Participante: Antonio Jimenez Martin//Participante: Juan Antonio Fdez Del Pozo De Salamanca//@HBP. HUMAN BRAIN PROJECT. SUBPROYECTO NEURONAL STRUCTURAL DESIGNThe human brain can be seen as an immensely powerful, energy efficient, selflearning, selfrepairing computer. If we could understand and mimic the way the brain works, we could revolutionize information technology, medicine and society. But to do so we have to bring together everything we know and everything we can learn about the inner workings of the brain's molecules, cells and circuits. The goal of the Human Brain Project (HBP) is to do this by integrating our knowledge in massive databases and in computer models of the brain. This will require breakthroughs in mathematics and software engineering and an international supercomputing facility more powerful than any before. This is all possible. Experimental and clinical data is accumulating exponentially. Computers powerful enough to meet the project?s initial requirements are already here.
An international team led by Europe?s best neuroscientists, doctors, physicists, mathematicians, computer engineers and ethicists have assembled to begin the mission. As technology progresses and the project discovers new principles of brain design it will build ever more realistic models to probe ever deeper principles. The benefits for society will be huge, even before the HBP achieves its final goals. Models of the brain will revolutionize information technology, allowing us to design computers, robots, sensors, prosthetics and other devices far more powerful, more intelligent and more energy efficient than today. They will help us understand the root causes of brain diseases, and to diagnose them early, when they can still be treated. They will reduce reliance on animal testing and make it easier to develop new cures for brain disease. They will help us understand how the brain ages, and how to slow these changes and nurture a healthy brain for our children. In summary, the HBP is poised to produce dramatic advances in technology, a new understanding of the way the brain works and a new ability to cure its diseases.
1Comisn europeaBELGICAGran Empresa (>250)
18/05/2011~Director: Pedro Maria Larraaga Mugica//Participante: Maria Concepcion Bielza Lozoya//Participante: Ignacio Leguey Vitoriano//HMINERIA DE DATOS CON PGMS: NUEVOS ALGORITMOS Y APLICACIONES. MDPGMSUPMProbabilistic graphical models (PGMs) are a competitive tool that allows discovering useful knowledge from data, and its posterior exploitation (by means of inference). Although the field of PGMs exhibits nowadays a high degree of maturity, more research is necessary to extend its applicability as a data mining tool to more complex problems in existing realworld applications, or solve new ones. In this project we propose a joint effort to advance to this research line, by means of a coordinated project formed by four groups that have previously demonstrated their research experience by making substantial contributions to the stateoftheart on PGMs, and with a high degree of interconnection acquired by previous coordinated projects and collaborations. The main objective of this project is to advance in different topics related to PGMs, so that we will obtain better results than previous approaches, both because we enlarge the class of inpractice solvable problems, or because we face new/recent challenges. The core package of the algorithms to be developed has a direct application in several stages of the Knowledge Discovering from Databases (KDD) cycle, as preprocessing, (supervised and unsupervised) data mining and knowledge exploitation (inference). Also, during the development of these new algorithms we must bear in mind a set of challenging real applications we have selected to be included (and that serve as testbeds) in this project."Ministerio de Innovacin y CienciavDirector: Maria Concepcion Bielza Lozoya//Participante: Pedro Maria Larraaga Mugica//Participante: Ruben Armaanzas Arnedillo//Participante: Juan Antonio Fdez Del Pozo De Salamanca//Participante: Pedro Luis Lopez Cruz//Participante: Hossein Karshenas Najafabadi//Participante: Bojan Mihaljevic //Participante: Luis Pelayo Guerra Velasco//Participante: Laura Anton Sanchez//ISBN
Tipo de TesisCalificacinFechaLContributions to Bayesian network learning with applications to neuroscienceDoctoralSobresaliente cum laudepAutor: Pedro Luis Lopez Cruz//Director: Pedro Maria Larraaga Mugica//Director: Maria Concepcion Bielza Lozoya//cMultidimensional classification using Bayesian networks for stationary and evolving streaming datakAutor: Hanen Borchani .//Director: Maria Concepcion Bielza Lozoya//Director: Pedro Maria Larraaga Mugica//RRegularized Model Learning in EDAs for Continuous and MultiObjective OptimizationwAutor: Hossein Karshenas Najafabadi//Director: Maria Concepcion Bielza Lozoya//Director: Pedro Maria Larraaga Mugica//JCR del ISITtulo de la revistaISSNFactor de impacto JCRInformacin de impactoVolumenDOINmero de revistaDesde la pginaHasta la pginaMesRankingRA new measure for gene expression biclustering based on nonparametric correlation,COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 016926071,51611210.1016/j.cmpb.2013.07.0753367397%Autor: Pedro Maria Larraaga Mugica//AAutor: jose l. flores //Autor: inaki inza //Autor: borja calvo //TA review on evolutionary algorithms in Bayesian network learning and inference tasksINFORMATION SCIENCES 002002552,83323310.1016/j.ins.2012.12.051109125Autor: Pedro Maria Larraaga Mugica//Autor: Hossein Karshenas Najafabadi//Autor: Maria Concepcion Bielza Lozoya//Autor: No Encontrado //A Survey of L1 RegressionL1 regularization, or < regularization with an L1 penalty, is a popular idea in statistics and machine learning. This paper reviews the concept and application of L1 regularization for regression. It is not our aim to present a comprehensive list of the utilities of the L1 penalty in the regression setting. Rather, we focus on what we believe is the set of most representative uses of this regularization technique, which we describe in some detail. Thus, we deal with a number of L1regularized methods for linear regression, generalized linear models, and time series analysis. Although this review targets practice rather than theory, we do give some theoretical details about L1penalized linear regression, usually referred to as the least absolute shrinkage and selection operator (lasso). INTERNATIONAL STATISTICAL REVIEW 030677340,548110.1111/insr.12023361387SIN MESLAutor: Maria Concepcion Bielza Lozoya//Autor: Pedro Maria Larraaga Mugica//Autor: diego vidaurre //eAN L1REGULARIZED NAIVE BAYESINSPIRED CLASSIFIER FOR DISCARDING REDUNDANT AND IRRELEVANT PREDICTORS6INTERNATIONAL JOURNAL ON ARTIFICIAL INTELLIGENCE TOOLS 021821300,2172210.1142/S021821301350019X418dAutor: No Encontrado //Autor: Maria Concepcion Bielza Lozoya//Autor: Pedro Maria Larraaga Mugica//^Analysis of scientific activity in Spanish public universities in the area of computer science,REVISTA ESPANOLA DE DOCUMENTACION CIENTIFICA 021006140,5743610.3989/redc.2013.1.91217jAutor: Alfonso Ibez Martn//Autor: Maria Concepcion Bielza Lozoya//Autor: Pedro Maria Larraaga Mugica//cBayesian network modeling of the consensus between experts: An application to neuron classificationONeuronal morphology is hugely variable across brain regions and species, and their classification strategies are a matter of intense debate in neuroscience. GABAergic cortical interneurons have been a challenge because it is difficult to find a set of morphological properties which clearly define neuronal types. A group of 48 neuroscience experts around the world were asked to classify a set of 320 cortical GABAergic interneurons according to the main features of their threedimensional morphological reconstructions. A methodology for building a model which captures the opinions of all the experts was proposed. First, one Bayesian network was learned for each expert, and we proposed an algorithm for clustering Bayesian networks corresponding to experts with similar behaviors. Then, a Bayesian network which represents the opinions of each group of experts was induced. Finally, a consensus Bayesian multinet which models the opinions of the whole group of experts was built. A thorough analysis of the consensus model identified different behaviors between the experts when classifying the interneurons in the experiment. A set of characterizing morphological traits for the neuronal types was defined by performing inference in the Bayesian multinet. These findings were used to validate the model and to gain some insights into neuron morphology..International Journal of Approximate Reasoning 0888613X1,948Datos JCR del ao 2011,http://dx.doi.org/10.1016/j.ijar.2013.03.011inpressAutor: Pedro Luis Lopez Cruz//Autor: Pedro Maria Larraaga Mugica//Autor: Javier De Felipe Oroquieta//Autor: Maria Concepcion Bielza Lozoya//%Bayesian Sparse Partial Least SquaresNEURAL COMPUTATION 089976671,762510.1162/NECO_a_005241233183339MAutor: diego vidaurre //Autor: marcel a. j. van gerven //Autor: tom heskes //EClassification of neocortical interneurons using affinity propagationFRONTIERS IN NEURAL CIRCUITS 166251105,098710.3389/fncir.2013.00185130Autor: laura m. mcgarry //Autor: rafael yuste //DClassification of neural signals from sparse autoregressive featuresNEUROCOMPUTING 092523121,5811110.1016/j.neucom.2012.12.0132126VCluster methods for assessing research performance: exploring Spanish computer scienceSCIENTOMETRICS 013891301,9669710.1007/s1119201309859571600jAutor: Alfonso Ibez Martn//Autor: Pedro Maria Larraaga Mugica//Autor: Maria Concepcion Bielza Lozoya//`Comparison of metaheuristic strategies for peakbin selection in proteomic mass spectrometry data22210.1016/j.ins.2010.12.013229246oAutor: Ruben Armaanzas Arnedillo//Autor: Maria Concepcion Bielza Lozoya//Autor: Pedro Maria Larraaga Mugica//Autor: miguel garciatorres //#Directional naive Bayes classifiers Directional data are ubiquitous in science. These data have some special properties that rule out the use of classical statistics. Therefore, different distributions and statistics, such as the univariate von Mises and the multivariate von MisesFisher distributions, should be used to deal with this kind of information. We extend the naive Bayes classifier to the case where the conditional probability distributions of the predictive variables follow either of these distributions. We consider the simple scenario, where only directional predictive variables are used, and the hybrid case, where discrete, Gaussian and directional distributions are mixed. The classifier decision functions and their decision surfaces are studied at length. Artificial examples are used to illustrate the behavior of the classifiers. The proposed classifiers are then evaluated over eight datasets, showing competitive performances against other naive Bayes classifiers that use Gaussian distributions or discretization to manage directional data.!Pattern Analysis And Applications 143375410,73910.1007/s100440130340zjAutor: Pedro Luis Lopez Cruz//Autor: Maria Concepcion Bielza Lozoya//Autor: Pedro Maria Larraaga Mugica//qLearning mixtures of polynomials of multidimensional probability densities from data using Bspline interpolationNonparametric density estimation is an important technique in probabilistic modeling and reasoning with uncertainty. We present a method for learning mixtures of polynomials (MoPs) approximations of onedimensional and multidimensional probability densities from data. The method is based on basis spline interpolation, where a density is approximated as a linear combination of basis splines. We compute maximum likelihood estimators of the mixing coefficients of the linear combination. The Bayesian information criterion is used as the score function to select the order of the polynomials and the number of pieces of the MoP. The method is evaluated in two ways. First, we test the approximation fitting. We sample artificial datasets from known onedimensional and multidimensional densities and learn MoP approximations from the datasets. The quality of the approximations is analyzed according to different criteria, and the new proposal is compared with MoPs learned with Lagrange interpolation and mixtures of truncated basis functions. Second, the proposed method is used as a nonparametric density estimation technique in Bayesian classifiers. Two of the most widely studied Bayesian classifiers, i.e., the naive Bayes and treeaugmented naive Bayes classifiers, are implemented and compared. Results on real datasets show that the nonparametric Bayesian classifiers using MoPs are comparable to the kernel densitybased Bayesian classifiers. We provide a free R package implementing the proposed methods.10.1016/j.ijar.2013.09.018VMachine Learning Approach for the Outcome Prediction of Temporal Lobe Epilepsy SurgeryEpilepsy surgery is effective in reducing both the number and frequency of seizures, particularly in temporal lobe epilepsy (TLE). Nevertheless, a significant proportion of these patients continue suffering seizures after surgery. Here we used a machine learning approach to predict the outcome of epilepsy surgery based on supervised classification data mining taking into account not only the common clinical variables, but also pathological and neuropsychological evaluations. We have generated models capable of predicting whether a patient with TLE secondary to hippocampal sclerosis will fully recover from epilepsy or not. The machine learning analysis revealed that ou< tcome could be predicted with an estimated accuracy of almost 90% using some clinical and neuropsychological features. Importantly, not all the features were needed to perform the prediction; some of them proved to be irrelevant to the prognosis. Personality style was found to be one of the key features to predict the outcome. Although we examined relatively few cases, findings were verified across all data, showing that the machine learning approach described in the present study may be a powerful method. Since neuropsychological assessment of epileptic patients is a standard protocol in the presurgical evaluation, we propose to include these specific psychological tests and machine learning tools to improve the selection of candidates for epilepsy surgery.PLOS ONE 193262034,092810.1371/journal.pone.00628199Autor: Ruben Armaanzas Arnedillo//Autor: Lidia Alonso Nanclares//Autor: Asta Kastanauskaite .//Autor: Javier De Felipe Oroquieta//Autor: Maria Concepcion Bielza Lozoya//Autor: Pedro Maria Larraaga Mugica//Autor: Jess DeFelipeOroquieta Departamento de Psicologa y Eduacin, Universidad Camilo Jos Cela, Villanueva de la Caada, Madrid, Spain//Autor: Rafael G. De Sola Department of Neurosurgery, Hospital Universitario de la Princesa, Madrid, Madrid, Spain//hMultiobjective estimation of distribution algorithm based on joint modeling of objectives and variables{This paper proposes a new multiobjective estimation of distribution algorithm (EDA) based on joint probabilistic modeling of objectives and variables. This EDA uses the multidimensional Bayesian network as its probabilistic model. In this way it can capture the dependencies between objectives, variables and objectives, as well as the dependencies learnt between variables in other Bayesian networkbased EDAs. This model leads to a problem decomposition that helps the proposed algorithm to find better tradeoff solutions to the multiobjective problem. In addition to Pareto set approximation, the algorithm is also able to estimate the structure of the multiobjective problem. To apply the algorithm to manyobjective problems, the algorithm includes four different ranking methods proposed in the literature for this purpose. The algorithm is first applied to the set of walking fish group (WFG) problems, and its optimization performance is compared with a standard multiobjective evolutionary algorithm and another competitive multiobjective EDA. The experimental results show that on several of these problems and for different objective space dimensions the proposed algorithm performs significantly better and achieves comparable results on some other, when compared with the other two algorithms. The algorithm is then tested on the set of CEC09 problems, where the results show that multiobjective optimization based on joint model estimation is able to obtain considerably better fronts for some of the problems comparing with the search based on conventional genetic operators in the stateoftheart multiobjective evolutionary algorithms.Ieee Transactions on Evolutionary Computation 1089778X4,81qAutor: Hossein Karshenas Najafabadi//Autor: Maria Concepcion Bielza Lozoya//Autor: Pedro Maria Larraaga Mugica//9Autor: Roberto Santana University of the Basque Country//FNetwork measures for information extraction in evolutionary algorithmsProblem domain information extraction is a critical issue in many realworld optimization problems. Increasing the repertoire of techniques available in evolutionary algorithms with this purpose is fundamental for extending the applicability of these algorithms. In this paper we introduce a unifying information mining approach for evolutionary algorithms. Our proposal is based on a division of the stages where structural modelling of the variables interactions is applied. Particular topological characteristics induced from different stages of the modelling process are identified. Network theory is used to harvest problem structural information from the learned probabilistic graphical models (PGMs). We show how different statistical measures, previously studied for networks from different domains, can be applied to mine the graphical component of PGMs. We provide evidence that the computed measures can be employed for studying problem difficulty, classifying different problem instances and predicting the algorithm behavior.;International Journal of Computational Intelligence Systems 187568831,47111631188XNew insights into the classification and nomenclature of cortical GABAergic interneurons8A systematic classification and accepted nomenclature of neuron types is much needed but is currently lacking. This article describes a possible taxonomical solution for classifying GABAergic interneurons of the cerebral cortex based on a novel, webbased interactive system that allows experts to classify neurons with predetermined criteria. Using Bayesian analysis and clustering algorithms on the resulting data, we investigated the suitability of several anatomical terms and neuron names for cortical GABAergic interneurons. Moreover, we show that supervised classification models could automatically categorize interneurons in agreement with experts' assignments. These results demonstrate a practical and objective approach to the naming, characterization and classification of neurons based on community consensus.NATURE REVIEWS NEUROSCIENCE 1471003X30,4451410.1038/nrn3444202216Autor: Javier De Felipe Oroquieta//Autor: Pedro Luis Lopez Cruz//Autor: Ruth Benavides Piccione//Autor: Maria Concepcion Bielza Lozoya//Autor: Pedro Maria Larraaga Mugica//qAutor: stewart anderson //Autor: andreas burkhalter //Autor: bruno cauli //Autor: alfonso fairen //Autor: dirk feldmeyer //Autor: gord fishell //Autor: david fitzpatrick //Autor: tamas f. freund //Autor: guillermo gonzalezburgos //Autor: shaul hestrin //Autor: sean hill //Autor: patrick r. hof //Autor: josh huang //Autor: yasuo kawaguchi //Autor: zoltan kisvarday //Autor: yoshiyuki kubota //Autor: david a. lewis //Autor: oscar marin //Autor: henry markram //Autor: chris j. mcbain //Autor: hanno s. meyer //Autor: hannah monyer //Autor: sacha b. nelson //Autor: kathleen rockland //Autor: jean rossier //Autor: john l. r. rubenstein //Autor: bernardo rudy //Autor: massimo scanziani //Autor: gordon m. shepherd //Autor: chet c. sherwood //Autor: jochen f. staiger //Autor: gabor tamas //Autor: alex thomson //Autor: yun wang //Autor: rafael yuste //Autor: giorgio a. ascoli //Parameter Control of Genetic Algorithms by Learning and Simulation of Bayesian Networks  A Case Study for the Optimal Ordering of TablesParameter setting for evolutionary algorithms is still an important issue in evolutionary computation. There are two main approaches to parameter setting: parameter tuning and parameter control. In this paper, we introduce selfadaptive parameter control of a genetic algorithm based on Bayesian network learning and simulation. The nodes of this Bayesian network are genetic algorithm parameters to be controlled. Its structure captures probabilistic conditional (in)dependence relationships between the parameters. They are learned from the best individuals, i.e., the best configurations of the genetic algorithm. Individuals are evaluated by running the genetic algorithm for the respective parameter configuration. Since all these runs are timeconsuming tasks, each genetic algorithm uses a smallsized population and is stopped before convergence. In this way promising individuals should not be lost. Experiments with an optimal search problem for simultaneous row and column orderings yield the same optima as stateoftheart methods but with a sharp reduction in computational time. Moreover, our approach can cope with as yet unsolved highdimensional problems.*JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY 100090000,5642810.1007/s1139001313700720731Autor: Maria Concepcion Bielza Lozoya//Autor: Juan Antonio Fdez Del Pozo De Salamanca//Autor: Pedro Maria Larraaga Mugica//YPredicting dementia development in Parkinson's disease using Bayesian net< work classifiers PSYCHIATRY RESEARCHNEUROIMAGING 092549272,964213!10.1016/j.pscychresns.2012.06.00129298pAutor: Dinora Araceli Morales Vega//Autor: Pedro Maria Larraaga Mugica//Autor: Maria Concepcion Bielza Lozoya//Autor: yolanda vivesgilabert //Autor: beatriz gomezanson //Autor: endika bengoetxea //Autor: javier pagonabarraga //Autor: jaime kulisevsky //Autor: idoia corcuerasolano //Autor: manuel delfino //hPredicting human immunodeficiency virus inhibitors using multidimensional. Bayesian network classifiersOur aim is to use multidimensional Bayesian network classifiers in order to predict the human immunodeficiency virus type 1 (HIV1) reverse transcriptase and protease inhibitors given an input set of respective resistance mutations that an HIV patient carries.
#ARTIFICIAL INTELLIGENCE IN MEDICINE 093336571,3455710.1016/j.artmed.2012.12.005219eAutor: Hanen Borchani .//Autor: Maria Concepcion Bielza Lozoya//Autor: Pedro Maria Larraaga Mugica//YAutor: Carlos Toro Department of Microbiology, Hospital Carlos III, Madrid 28029, Spain//<Regularized continuous estimation of distribution algorithmsAPPLIED SOFT COMPUTING 156849462,61210.1016/j.asoc.2012.11.049524122432Autor: Hossein Karshenas Najafabadi//Autor: No Encontrado //Autor: Maria Concepcion Bielza Lozoya//Autor: Pedro Maria Larraaga Mugica//Relationship among research collaboration, number of documents and number of citations: a case study in Spanish computer science production in 200020099510.1007/s1119201208836689716#Sparse regularized local regression(COMPUTATIONAL STATISTICS & DATA ANALYSIS 016794731,0286210.1016/j.csda.2013.01.008122135dUnveiling relevant nonmotor Parkinson's disease severity symptoms using a machine learning approach5810.1016/j.artmed.2013.04.002195=Autor: kallol ray chaudhuri //Autor: pablo martinezmartin //ISSN o ISBNEntidad relacionadaLugar del congreso>Bayesian Networks to Answer Challenging Neuroscience QuestionsSummary form only given. In this keynote lecture we will show how Bayesian networks can address important neuroscience problems. These problems include: (a) neuroanatomy issues, like modeling and simulation of dendritic trees and classifying neuron types based on morphological features; (b) neurodegenerative diseases, like predicting healthrelated quality of life in Parkinson's disease, classification of dementia stages in Parkinson's disease and searching for genetic biomarkers in Alzheimer's disease.13829652P26th IEEE International Symposium on ComputerBased Medical Systems (CBMS2013),Sin nacionalidadOportoLAutor: Pedro Maria Larraaga Mugica//Autor: Maria Concepcion Bielza Lozoya//Nombre congresoEntidad organizadoraLugar/Ciudad de imparticinFecha inicio Fecha fin1Advanced Statistics and Data Mining Summer SchoolAn intensive set of courses providing attendees with an introduction to the theoretical foundations as well as the practical applications of some of the modern statistical analysis techniques currently in useUPMMadrid
24/06/2013
05/07/2013Autor: Maria Concepcion Bielza Lozoya//Autor: Pedro Maria Larraaga Mugica//Autor: Ruben Armaanzas Arnedillo//Autor: Bojan Mihaljevic //Autor: Luis Pelayo Guerra Velasco//Autor: Pedro Luis Lopez Cruz//Autor: Laura Anton Sanchez///Bayesian Networks in Computational NeuroscienceNeuroscience faces challenging problems that require new machine learning methods. In this tutorial we will present some realworld examples where Bayesian network models fit after some adaptation. These problems include: (a) neuroanatomy issues, like modeling and simulation of dendritic trees, inferring electrophysiological behavior from morphological neuron characteristics, and classifying neuron and spine types based on morphological features; (b) neurodegenerative diseases, like predicting healthrelated quality of life in Parkinson's disease, classification of dementia stages in Parkinsons disease and searching for genetic biomarkers in Alzheimer's disease.@14th Conference on Artificial Intelligence in Medicine AIME 2013Universidad de MurciaMurcia
29/05/2013
01/06/2013Entidad InformeYMiembro de Comits Cientficos. IEEE Congress on Evolutionary Computation (IEEE CEC2013)IEEE Congress on Evolutionary Computation (IEEE CEC2013). Special Session of Evolutionary Algorithms with Statistical & Machine Learning Techniques. Cancn (Mxico)xMiembro de Comits Cientficos. International WorkConference on Bioinformatics and Biomedical Engineering (IWBBIO2013)`International WorkConference on Bioinformatics and Biomedical Engineering (IWBBIO2013) GranadaMiembro de Comits Cientficos. 12th European Conference on Symbolic and Quantitative approaches to Reasoning with Uncertainty (ECSQARU2013)12th European Conference on Symbolic and Quantitative approaches to Reasoning with Uncertainty (ECSQARU2013) Utrecht (Holanda)aMiembro de Comits Cientficos. 2013 Genetic and Evolutionary Computation Conference (GECCO2013)2013 Genetic and Evolutionary Computation Conference (GECCO2013) Estimation of Distribution Algotihms Track. Amsterdam (Holanda)iMiembro de Comits Cientficos. 23rd International Joint Conference on Artificial Intelligence (IJCAI13)Z 23rd International Joint Conference on Artificial Intelligence (IJCAI13) Beijing (China)*Revisora de Libros. Bayesian Networks in RiBayesian Networks in R
Autores: Marco Scutari and JeanBaptiste Denis
Editorial: Chapman & Hall/CRC Press'Autor: Maria Concepcion Bielza Lozoya//6Revisora de revista JCR. Annals of Operations Research:Revisora de revista JCR. Journal of Biomedical InformaticsEdicin del LibroEditorial del LibroNmero de pginas del libroSeriebActas de la XV Conferencia de la Asociacin Espaola para la Inteligencia Artificial (CAEPIA 2013)UActas de la XV Conferencia de la Asociacin Espaola para la Inteligencia Artificial CEDI9788469583487158Autor: Antonio Samlern //#Advances in Artificial Intelligence15th Conference of the Spanish Association
for Artificial Intelligence, CAEPIA 2013
Madrid, Spain, September 1720, 2013
Proceedings10.1007/9783642406430Springer9783642406423404 LNAI 8109Autor: Antonio Salmern //Autor: Amparo AlonsoBetanzos //Autor: J.Ignacio Hidalgo //Autor: Luis Martinez //Autor: Alicia Troncoso //Autor: Emilio Corchado //Autor: Juan M. Corchado //EntidadLugarPginasReferencia/URLTipo de publicacin6Avances en el Pronstico de la Ciruga de la EpilepsiaBArtculo de divulgacin publicado en Madri+d Noticias y Fsica HoyArtculo de divulgacin8Autor: Jess DeFelipeOroquieta //Autor: R.G. de Sola //uChanging conduction delays to maximize the number of polychronous groups with an estimation of distribution algorithm1UPMFI/DIA/20131, Technical University of MadridTechnical ReportsCDesarrollan un Kit que Predice la Supervivencia al Cncer de PulmnArtculo de Divulgacin publicado en Madri+d Noticias, Plataorma SINC de la FECYT (Servicio de Informacin y Noticias Cientficas), AlphaGalileo, EuropaPress, 20minutos...Artculo de DivulgacinTipo de participacin RevisoresISBN o ISSNFecha inicio congresoFecha fin congresoTtulo de las actas%Augmented Seminaive Bayes Classifier}The naive Bayes is a competitive classifier that makes strong conditional independence assumptions. Its accuracy can be improved by relaxing these assumptions. One classifier which does that is the seminaive Bayes. The stateoftheart algorithm for learning a seminaive Bayes from data is the backward sequential elimination and joining (BSEJ) algorithm. We extend BSEJ with a second step which removes some of its unwarranted independence assumptions. Our classifier outperforms BSEJ and five other Bayesian network classifiers on a set of benchmark databases, although the difference in performance is not statistically significant.HXV Conferencia de la Asociacin Espaola para la Inteligencia Artificial960
17/09/2013
20/09/2013159< 167Advances in Artificial Intelligence, Proceedings of the 15th MultiConference of the Spanish Association for Artificial Intelligence, volume 8109 of Lecture Notes in Computer SciencefAutor: Bojan Mihaljevic //Autor: Pedro Maria Larraaga Mugica//Autor: Maria Concepcion Bielza Lozoya//BBayesClass: An R package for learning Bayesian network classifiers6An R package for learning Bayesian network classifiersuseR!Albacete
10/07/2013
12/07/201353fAutor: Bojan Mihaljevic //Autor: Maria Concepcion Bielza Lozoya//Autor: Pedro Maria Larraaga Mugica//PLearning conditional linear Gaussian classifiers with probabilistic class labelsWe study the problem of learning Bayesian classifiers (BC) when the true class label of the training instances is not known, and is substituted by a probability distribution over the class labels for each instance. This scenario can arise, e.g., when a group of experts is asked to individually provide a class label for each instance. We particularize the generalized expectation maximization (GEM) algorithm in (Come et al., 2009, Pattern Recognition 42: 334348) to learn BCs with different structural complexities: naive Bayes, averaged onedependence estimators or general conditional linear Gaussian classifiers. An evaluation conducted on eight datasets shows that BCs learned with GEM perform better than those using either the classical Expectation Maximization algorithm or potentially wrong class labels. BCs achieve similar results to the multivariate Gaussian classifier without having to estimate the full covariance matrices.10.1007/9783642406430_15139148CLearning mixtures of polynomials of conditional densities from dataMixtures of polynomials (MoPs) are a nonparametric density estimation technique for hybrid Bayesian networks with continuous and discrete variables. We propose two methods for learning MoP approximations of conditional densities from data. Both approaches are based on learning MoP approximations of the joint density and the marginal density of the conditioning variables, but they differ as to how the MoP approximation of the quotient of the two densities is found. We illustrate the methods using data sampled from a simple Gaussian Bayesian network. We study and compare the performance of these methods with the approach for learning mixtures of truncated basis functions from data. 10.1007/9783642406430_37363372Autor: Thomas D. Nielsen Aalborg University//KSemisupervised projected clustering for classifying GABAergic interneuronsA systematic classification of neuron types is a critical topic of debate in neuroscience. In this study, we propose a semisupervised projected clustering algorithm based on finite mixture models and the expectationmaximization (EM) algorithm, that is useful for classifying neuron types. Specifically, we analyzed cortical GABAergic interneurons from different animals and cortical layers. The new algorithm, called SeSProC, is a probabilistic approach for classifying known classes and for discovering possible new groups of interneurons. Basic morphological features containing information about axonal and dendritic arborization sizes and orientations are used to characterize the interneurons. SeSProC also identifies the relevance of each feature and group separately. This article aims to present the methodological approach, reporting results for known classes and possible new groups of interneurons.#Artificial Intelligence in Medicine9783642383250156168j14th Conference on Artificial Intelligence in Medicine, AIME 2013
Murcia, Spain, May/June 2013
ProceedingsAutor: Luis Pelayo Guerra Velasco//Autor: Ruth Benavides Piccione//Autor: Maria Concepcion Bielza Lozoya//Autor: Victor Robles Forcada//Autor: Javier De Felipe Oroquieta//Autor: Pedro Maria Larraaga Mugica//HSpatial Analysis of Dendritic Spines Distribution with Bayesian Networks_Understanding the brain is one of the greatest challenges of our time. This understanding would provide new techniques for diagnosis and treatment of brain diseases. Dendritic surfaces of pyramidal cells (the most common neuron in the cerebral cortex) are covered by small protrusions named dendritic spines. These represent the targets of most excitatory synapses in the cerebral cortex and therefore, dendritic spines prove critical in learning, memory and cognition; moreover, loss or alteration of these structures has been described in the pathogenesis of major neurological disorders such as Alzheimer's disease. Recent studies also suggest selective alterations in spines with aging in humans. For those reasons, it seems reasonable to think that spatial distribution could be related in some way to spine interaction. Our goal is the development of techniques that allow us to exploit the capabilities offered by Bayesian networks, using them with elements of spatial statistics, to study this spatial distribution of dendritic spines. With this, we hope we could help to answer questions about brain function.
W14th Conference on Artificial Intelligence in Medicine (AIME 2013)
Doctoral Consortium
45614th Conference on Artificial Intelligence in MedicinehAutor: Laura Anton Sanchez//Autor: Maria Concepcion Bielza Lozoya//Autor: Pedro Maria Larraaga Mugica//MTowards optimal neuronal wiring through estimation of distribution algorithmsOne of the greatest challenges of our time is to understand brain functions. Our goal is to study the existence of an optimal neuronal design, defined as the one that has a minimum total wiring. Many researchers have studied the problem of optimal wiring in neuronal trees. Here we propose a new approach. We start from point clouds formed by the branching points of real neuronal trees and we search for the optimal forest from these point clouds. To do this, we formalize the problem as a forest of degree constrained minimum spanning trees (DCMST). Since the DCMST problem is NPhard, we will try to solve it using estimation of distribution algorithms, particularly in permutation domains.0Genetic and evolutionary computation conference Amsterdam9781450319645
06/07/201316471650oGECCO '13 Companion Proceedings of the 15th annual conference companion on Genetic and evolutionary computationhAutor: Laura Anton Sanchez//Autor: Pedro Maria Larraaga Mugica//Autor: Maria Concepcion Bielza Lozoya//EstadoReferencia Patente PrioritariaEn explotacinLicenciatariosFecha solicitudReferencia PCTReferencia EPOReferencia EEUUReferencia JaponTitulares aparte de la UPMCTest predictor de supervivencia global de adenocarcinoma del pulmn Concedida
P201031626
05/11/20102CIEMAT  Centro de Investigaciones Energticas, M;[Inventor Contacto: Pedro Maria Larraaga Mugica//Inventor: Maria Concepcion Bielza Lozoya//=Inventor: Jess Mara Paramio Gonzlez CIEMAT  Centro de Investigaciones Energticas, Medioambientales y Tecnolgicas (MEC  Ministerio de Educacin y Ciencia)//Inventor: Ramn Garca Escudero CIEMAT  Centro de Investigaciones Energticas, Medioambientales y Tecnolgicas (MEC  Ministerio de Educacin y Ciencia)//Comentarios MritovOrganizacin de congresos y eventos. Jornadas CientficoTcnicas y Seminario Doctoral de la Red ATICA (SEMATICA2013)QJornadas CientficoTcnicas y Seminario Doctoral de la Red ATICA (SEMATICA2013)XCoorganizadores: cuatro grupos de investigacin de la red ATICA de UCM, UPC, UC3M y UPMOrganizacin de congresos y eventos. Presidenta del Comit Cientfico de la XV Conferencia de la Asociacin Espaola para la Inteligencia Artificial (CAEPIA'13){Presidenta del Comit Cientfico de la XV Conferencia de la Asociacin Espaola para la Inteligencia Artificial (CAEPIA'13)<Coorganizadores: Antonio Salmern (Almera), VicepresidenteEntidad premiadaEntidad concedenteLugar donde se premi3Aritmel prize, Spanish Scientific Computing SocietyHBest Paper Award: 14th Conference on Artificial Intelligence in Medicine\Best student paper: 15th Annual Genetic and Evolutionary Computation Conference, GECCO 2013 TipoEConferencia de la Asociacin Espaola para l<;a Inteligencia ArtificialjMiembro del comit de programa de la Conferencia de la Asociacin Espaola para la Inteligencia ArtificialCResponsabilidades en congresos y reuniones tcnicas internacionales#Autor: Ruben Armaanzas Arnedillo//3IEEE Symposium Series on Computational IntelligencegMiembro del comit de programa del congreso IEEE Symposium Series on Computational Intelligence SSCI'13
25/04/2013SingapurEInternational Conference on Adaptive and Natural Computing AlgorithmsMiembro del comit de programa de la conferencia International Conference on Adaptive and Natural Computing Algorithms ICANNGA'13
04/04/2013Lausanne (Suiza)JInternational WorkConference on Bioinformatics and Biomedical EngineeringMiembro del comit de programa del congreso International WorkConference on Bioinformatics and Biomedical Engineering IWBBIO'13
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