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D-Index & Metrics

Computer Science

D-Index
40
Citations
8940
World Ranking
9155
National Ranking
140

Overview

Concha Bielza is affiliated with the Technical University of Madrid in Spain. Their research primarily focuses on computer science, with a significant number of publications in artificial intelligence and several related subfields.

The main fields of study for Bielza include:

  • Computer Science

Within computer science, Bielza has specialized in multiple subfields:

  • Artificial Intelligence
  • Molecular Biology
  • Signal Processing
  • Control and Systems Engineering
  • Computational Theory and Mathematics

Their main research topics are centered on probabilistic and machine learning methodologies. These topics include:

  • Bayesian Modeling and Causal Inference
  • Bayesian Methods and Mixture Models
  • Data Stream Mining Techniques
  • Time Series Analysis and Forecasting
  • Machine Learning and Data Classification
  • Machine Learning in Healthcare
  • Neural Networks and Applications

Bielza has contributed to a range of recent publications, some of which are:

  • "Bayesian networks for interpretable machine learning and optimization" (2021) published in Neurocomputing
  • "Multi-dimensional Bayesian network classifiers: A survey" (2020) published in Artificial Intelligence Review
  • "Machine-tool condition monitoring with Gaussian mixture models-based dynamic probabilistic clustering" (2020) published in Engineering Applications of Artificial Intelligence
  • "Long-term forecasting of multivariate time series in industrial furnaces with dynamic Gaussian Bayesian networks" (2021) published in Engineering Applications of Artificial Intelligence
  • "Identifying Parkinson's disease subtypes with motor and non-motor symptoms via model-based multi-partition clustering" (2021) published in Scientific Reports

Frequent coauthors include:

  • Pedro Larrañaga
  • Vicente P. Soloviev
  • Carlos Puerto-Santana
  • Bojan Mihaljević

Bielza's work appears regularly in select publication venues. These include:

  • bioRxiv (Cold Spring Harbor Laboratory)
  • arXiv (Cornell University)
  • Neurocomputing
  • Engineering Applications of Artificial Intelligence
  • Nature Neuroscience

The scientist has authored books published by Cambridge University Press, including "Data-Driven Computational Neuroscience" (2020).

Best Publications

  • Machine learning in bioinformatics

    Pedro Larrañaga;Borja Calvo;Roberto Santana;Concha Bielza

  • New insights into the classification and nomenclature of cortical GABAergic interneurons

    Javier DeFelipe;Pedro L. López-Cruz;Ruth Benavides-Piccione;Ruth Benavides-Piccione;Concha Bielza

  • A survey on multi-output regression

    Hanen Borchani;Gherardo Varando;Concha Bielza;Pedro Larrañaga

  • Discrete Bayesian Network Classifiers: A Survey

    Concha Bielza;Pedro Larrañaga

  • Multi-dimensional classification with Bayesian networks

    C. Bielza;G. Li;P. Larraòaga

  • Comparison of Bayesian networks and artificial neural networks for quality detection in a machining process

    M. Correa;C. Bielza;J. Pamies-Teixeira

  • A community-based transcriptomics classification and nomenclature of neocortical cell types

    Rafael Yuste;Michael Hawrylycz;Nadia Aalling;Argel Aguilar-Valles

  • A review on evolutionary algorithms in Bayesian network learning and inference tasks

    Pedro LarrañAga;Hossein Karshenas;Concha Bielza;Roberto Santana

  • Bayesian networks in neuroscience: a survey.

    Concha Bielza;Pedro Larrañaga

  • Bayesian chain classifiers for multidimensional classification

    Julio H. Zaragoza;L. Enrique Sucar;Eduardo F. Morales;Concha Bielza

  • A Survey of L 1 Regression

    Diego Vidaurre;Concha Bielza;Pedro Larrañaga

  • Multi-label classification with Bayesian network-based chain classifiers

    L. Enrique Sucar;Concha Bielza;Eduardo F. Morales;Pablo Hernandez-Leal

  • A review of estimation of distribution algorithms in bioinformatics

    Rubén Armañanzas;Iñaki Inza;Roberto Santana;Yvan Saeys

  • Parkinson's Disease Subtypes Identified from Cluster Analysis of Motor and Non-motor Symptoms

    Jesse Mu;Kallol Ray Chaudhuri;Concha Bielza;Jesús De Pedro-Cuesta

  • Comparison between supervised and unsupervised classifications of neuronal cell types: a case study.

    Luis Guerra;Laura M McGarry;Víctor Robles;Concha Bielza

  • Multiobjective Estimation of Distribution Algorithm Based on Joint Modeling of Objectives and Variables

    Hossein Karshenas;Roberto Santana;Concha Bielza;Pedro Larrañaga

  • Predicting dementia development in Parkinson's disease using Bayesian network classifiers

    Dinora A. Morales;Yolanda Vives-Gilabert;Beatriz Gómez-Ansón;Endika Bengoetxea

  • Bayesian networks for interpretable machine learning and optimization

    Bojan Mihaljević;Concha Bielza;Pedro Larrañaga

  • A review on probabilistic graphical models in evolutionary computation

    Pedro Larrañaga;Hossein Karshenas;Concha Bielza;Roberto Santana

  • A Comparison of Graphical Techniques for Asymmetric Decision Problems

    Concha Bielza;Prakash P. Shenoy

  • Akaike Information Criterion

    Pedro Larrañaga;Concha Bielza

  • A community-based transcriptomics classification and nomenclature of neocortical cell types.

    Rafael Yuste;Michael Hawrylycz;Nadia Aalling;Detlev Arendt

Frequent Co-Authors

Pedro Larrañaga
Pedro Larrañaga Technical University of Madrid
Javier DeFelipe
Javier DeFelipe Technical University of Madrid
Rafael Yuste
Rafael Yuste Columbia University
Jose A. Lozano
Jose A. Lozano Basque Center for Applied Mathematics
Richard H. Scheuermann
Richard H. Scheuermann J. Craig Venter Institute
Giorgio A. Ascoli
Giorgio A. Ascoli George Mason University
Oscar Marín
Oscar Marín King's College London
Gábor Tamás
Gábor Tamás University of Szeged
Dirk Feldmeyer
Dirk Feldmeyer Forschungszentrum Jülich

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