Now showing items 1-20 of 104

  • A decision making support tool: The resilience management fuzzy controller 

    González Cardenas, Rubén; Nebot Castells, M. Àngela; Múgica Álvarez, Francisco; Vellido Alcacena, Alfredo (Institute of Electrical and Electronics Engineers (IEEE), 2017)
    Conference report
    Open Access
    In this paper a fuzzy controller capable to perform an automated estimation of the period of time necessary to recover a resilience level is proposed. Estimations where made by considering realistic time-dependent action ...
  • Advances in clustering and visualization of time series using GTM through time 

    Olier Caparroso, Iván; Vellido Alcacena, Alfredo (2008-09)
    Article
    Restricted access - publisher's policy
    Most of the existing research on multivariate time series concerns supervised forecasting problems. In comparison, little research has been devoted to their exploration through unsupervised clustering and visualization. ...
  • Advances in machine learning and computational intelligence 

    Schleif, Frank-Michael; Biehl, Michael; Vellido Alcacena, Alfredo (2009-03)
    Article
    Restricted access - publisher's policy
  • Advances in semi-supervised alignment-free classification of G protein-coupled receptors 

    Cruz Barbosa, Raúl; Vellido Alcacena, Alfredo; Giraldo, Jesús (2013)
    Conference report
    Open Access
    G Protein-coupled receptors (GPCRs) are integral cell membrane proteins of great relevance for pharmacology due to their role in transducing extracellular signals. The 3-D s tructure is unknown for most of them, and the ...
  • A machine learning pipeline for supporting differentiation of glioblastomas from single brain metastases 

    Mocioiu, Victor; de Barros, Nuno M. Pedrosa; Ortega Martorell, Sandra; Slotboom, Johannes; Knecht, Urspeter; Arús, Carles; Vellido Alcacena, Alfredo; Julià Sapé, Margarida (I6doc.com, 2016)
    Conference report
    Open Access
    Machine learning has provided, over the last decades, tools for knowledge extraction in complex medical domains. Most of these tools, though, are ad hoc solutions and lack the systematic approach that would be required to ...
  • A MAP approach for convex non-negative matrix factorization in the diagnosis of brain tumors 

    Vilamala Muñoz, Albert; Belanche Muñoz, Luis Antonio; Vellido Alcacena, Alfredo (2014)
    Conference report
    Restricted access - publisher's policy
    Convex non-negative matrix factorization is a blind signal separation technique that has previously demonstrated to be well-suited for the task of human brain tumor diagnosis from magnetic resonance spectroscopy data. This ...
  • A methodological approach for algorithmic composition systems' parameter spaces aesthetic exploration 

    Paz Ortiz, Iván; Nebot Castells, M. Àngela; Romero Merino, Enrique; Múgica Álvarez, Francisco; Vellido Alcacena, Alfredo (Institute of Electrical and Electronics Engineers (IEEE), 2017)
    Conference lecture
    Open Access
    Algorithmic composition is the process of creating musical material by means of formal methods. As a consequence of its design, algorithmic composition systems are (explicitly or implicitly) described in terms of parameters. ...
  • A probabilistic approach to the visual exploration of G protein-coupled receptor sequences 

    Vellido Alcacena, Alfredo; Cárdenas, Martha Ivón; Olier Caparroso, Iván; Rovira, Xavier; Giraldo, Jesús (2011)
    Conference report
    Open Access
    The study of G protein-coupled receptors (GPCRs) is of great interest in pharmaceutical research, but only a few of their 3D structures are known at present. On the contrary, their amino acid sequences are known and ...
  • A Quotient Basis Kernel for the prediction of mortality in severe sepsis patients 

    Ribas Ripoll, Vicent; Romero Merino, Enrique; Ruiz Rodríguez, Juan Carlos; Vellido Alcacena, Alfredo (2013)
    Conference report
    Open Access
    In this paper, we describe a novel kernel for multinomial distributions, namely the Quotient Basis Kernel (QBK), which is based on a suitable reparametrization of the input space through algebraic geometry and statistics. ...
  • Artificial intelligence for the artificial kidney: Pointers to the future of a personalized hemodialysis therapy 

    Hueso, Miguel; Vellido Alcacena, Alfredo; Montero, Nuria; Barbieri, Carlo; Ramos, Rosa; Angoso, Manuel; Cruzado, Josep M; Jonsson, Anders (2018-02)
    Article
    Open Access
    Current dialysis devices are not able to react when unexpected changes occur during dialysis treatment, or to learn about experience for therapy personalization. Furthermore, great efforts are dedicated to develop miniaturized ...
  • Automated classification of brain tumours from short echo time in vivo MRS data using Gaussian decomposition and Bayesian neural networks 

    Arizmendi Pereira, Carlos Julio; Sierra Bueno, Daniel Alfonso; Vellido Alcacena, Alfredo; Romero Merino, Enrique (2014-09)
    Article
    Restricted access - publisher's policy
    Neuro-oncologists must ultimately rely on their acquired knowledge and accumulated experience to undertake the sensitive task of brain tumour diagnosis. This task strongly depends on indirect, non-invasive measurements, ...
  • Automated quality control for proton magnetic resonance spectroscopy data using convex non-negative matrix factorization 

    Mocioiu, Victor; Kyathanahally, Sreenath P.; Arús, Carles; Vellido Alcacena, Alfredo; Julià Sapé, Margarida (Springer, 2016)
    Conference report
    Open Access
    Proton Magnetic Resonance Spectroscopy (1H MRS) has proven its diagnostic potential in a variety of conditions. However, MRS is not yet widely used in clinical routine because of the lack of experts on its diagnostic ...
  • A variational Bayesian formulation for GTM: Theoretical foundations 

    Olier Caparroso, Iván; Vellido Alcacena, Alfredo (2007-09)
    External research report
    Open Access
    Generative Topographic Mapping (GTM) is a non-linear latent variable model of the manifold learning family that provides simultaneous visualization and clustering of high-dimensional data. It was originally formulated as ...
  • A variational formulation for GTM through time 

    Olier Caparroso, Iván; Vellido Alcacena, Alfredo (IEEE, 2008)
    Conference report
    Open Access
    Generative Topographic Mapping (GTM) is a latent variable model that, in its original version, was conceived to provide clustering and visualization of multivariate, realvalued, i.i.d. data. It was also extended to deal ...
  • A variational formulation for GTM through time: Theoretical foundations 

    Olier Caparroso, Iván; Vellido Alcacena, Alfredo (2007-10)
    External research report
    Open Access
    Generative Topographic Mapping (GTM) is a latent variable model that, in its standard version, was conceived to provide clustering and visualization of multivariate, real-valued, i.i.d. data. It was also extended to deal ...
  • A weighted Cramér’s V Index for the assessment of stability in the fuzzy clustering of class C G protein-coupled receptors 

    Vellido Alcacena, Alfredo; Halka, Christiana; Nebot Castells, M. Àngela (Springer, 2015)
    Conference report
    Open Access
    After decades of intensive use, K-Means is still a common choice for crisp data clustering in real-world applications, particularly in biomedicine and bioinformatics. It is well-known that different initializations of the ...
  • A weighted Cramer's V index for the assessment of stability in the fuzzy clustering of class C G protein-coupled receptors 

    Vellido Alcacena, Alfredo; Halka, Christiana; Nebot Castells, M. Àngela (Springer, 2015)
    Part of book or chapter of book
    Open Access
    After decades of intensive use, K-Means is still a common choice for crisp data clustering in real-world applications, particularly in biomedicine and bioinformatics. It is well-known that different initializations of the ...
  • Bayesian semi non-negative matrix factorisation 

    Vilamala Muñoz, Albert; Vellido Alcacena, Alfredo; Belanche Muñoz, Luis Antonio (I6doc.com, 2016)
    Conference report
    Open Access
    Non-negative Matrix Factorisation (NMF) has become a standard method for source identification when data, sources and mixing coefficients are constrained to be positive-valued. The method has recently been extended to allow ...
  • Big data analytics for obesity prediction 

    Bilal, Hasan; Vellido Alcacena, Alfredo; Ribas Ripoll, Vicent (IOS Press, 2018)
    Part of book or chapter of book
    Open Access
    Feature selection (FS) is essential for the analysis of genomic datasets with millions of features. In such context, Big Data tools are paramount, but the use of standard machine learning models is limited for data with ...
  • Bioinformatics and medicine in the era of deep learning 

    Bacciu, Davide; Lisboa, Paulo J G; Martín, José David; Stoean, Ruxandra; Vellido Alcacena, Alfredo (I6doc.com, 2018)
    Conference report
    Restricted access - publisher's policy
    Many of the current scientific advances in the life sciences have their origin in the intensive use of data for knowledge discovery. In no area this is so clear as in bioinformatics, led by technological breakthroughs in ...