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Ara es mostren els items 21-40 de 129

    • 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)
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      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)
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      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 ...

    • Bayesian semi non-negative matrix factorisation 

      Vilamala Muñoz, Albert; Vellido Alcacena, Alfredo; Belanche Muñoz, Luis Antonio (I6doc.com, 2016)
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      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)
      Capítol de llibre
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      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)
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      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 ...

    • Blood pressure assessment with differential pulse transit time and deep learning: a proof of concept 

      Ribas Ripoll, Vicent; Vellido Alcacena, Alfredo (Karger, 2019-02)
      Article
      Accés obert
      Modern clinical environments are laden with technology devices continuously gathering physiological data from patients. This is especially true in critical care environments, where life-saving decisions may have to be made ...

    • Capturing the dynamics of multivariate time series through visualization using generative topographic mapping through time 

      Olier, Ivan; Vellido Alcacena, Alfredo (2005-11)
      Report de recerca
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      Most of the existing research on time series concerns supervised forecasting problems. In comparison, little research has been devoted to unsupervised methods for the visual exploration of multivariate time series. In this ...

    • Cartogram representation of the batch-SOM magnification factor 

      Tosi, Alessandra; Vellido Alcacena, Alfredo (2012)
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      Model interpretability is a problem of knowledge extraction from the patterns found in raw data. One key source of knowledge is information visualization, which can help us to gain insights into a problem through graphical ...

    • Classification, dimensionality reduction, and maximally discriminatory visualization of a multicentre 1H-MRS database of brain tumors 

      Lisboa, Paulo J.G.; Romero Merino, Enrique; Vellido Alcacena, Alfredo; Julià Sapé, Margarida; Arús, Carles (IEEE, 2008)
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      The combination of an Artificial Neural Network classifier, a feature selection process, and a novel linear dimensionality reduction technique that provides a data projection for visualization and which preserves completely ...

    • Classifying malignant brain tumours from 1H-MRS data using Breadth Ensemble Learning 

      Vilamala Muñoz, Albert; Belanche Muñoz, Luis Antonio; Vellido Alcacena, Alfredo (Institute of Electrical and Electronics Engineers (IEEE), 2012)
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      In neuro oncology, the accurate diagnostic identification and characterization of tumours is paramount for determining their prognosis and the adequate course of treatment. This is usually a difficult problem per se, due ...

    • Clustering and visualization of multivariate time series 

      Vellido Alcacena, Alfredo; Olier Caparroso, Iván (Information Science Reference, 2009)
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      The exploratory investigation of multivariate time series (MTS) may become extremely difficult, if not impossible, for high dimensional datasets. Paradoxically, to date, little research has been conducted on the exploration ...

    • Comparative diagnostic accuracy of linear and nonlinear feature extraction methods in a neuro-oncology problem 

      Cruz Barbosa, Raúl; Bautista Villavicencio, David; Vellido Alcacena, Alfredo (Springer, 2011)
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      The diagnostic classification of human brain tumours on the basis of magnetic resonance spectra is a non-trivial problem in which dimensionality reduction is almost mandatory. This may take the form of feature selection ...

    • Comparative evaluation of semi-supervised geodesic GTM 

      Cruz Barbosa, Raúl; Vellido Alcacena, Alfredo (2009)
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      In many real problems that ultimately require data classification, not all the class labels are readily available. This concerns the field of semi-supervised learning, in which missing class labels must be inferred from ...

    • Complementing kernel-based visualization of protein sequences with their phylogenetic tree 

      Cárdenas, Martha Ivón; Vellido Alcacena, Alfredo; Olier, Iván; Rovira, Xavier; Giraldo, Jesús (Springer, 2012)
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    • Convex non-negative matrix factorization for brain tumor delimitation from MRSI data 

      Ortega Martorell, Sandra; Lisboa, Paulo J.G.; Vellido Alcacena, Alfredo; Simoes, Rui V.; Pumarola, Martí; Julià Sapé, Margarida; Arús, Carles (2012-10-23)
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    • Customer continuity management as a foundation for churn data mining 

      García, David L.; Vellido Alcacena, Alfredo; Nebot Castells, M. Àngela (2007-01)
      Report de recerca
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      This report lays the first theoretical foundations for a research program on analytical churn management. In the current hypercompetitive business scenario, firms have to bend over backwards in their strategies both to ...

    • Data mining in cancer research 

      Lisboa, Paulo J.G.; Vellido Alcacena, Alfredo; Tagliaferri, Roberto; Napolitano, Francesco; Ceccarelli, Michelle; Martín Guerrero, José D.; Biganzoli, Elia (2010-02)
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      This article is not intended as a comprehensive survey of data mining applications in cancer. Rather, it provides starting points for further, more targeted, literature searches, by embarking on a guided tour of computational ...

    • Discovering hidden pathways in bioinformatics 

      Lisboa, Paulo J.G.; Jarman, Ian H.; Etchells, Terence A.; Chambers, Simon J.; Bacciu, Davide; Whittaker, Joe; Garibaldi, Jon M.; Ortega Martorell, Sandra; Vellido Alcacena, Alfredo; Ellis, Ian O. (Springer, 2012)
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    • Discriminant convex non-negative matrix factorization for the classification of human brain tumours 

      Vilamala Muñoz, Albert; Lisboa, Paulo J G; Ortega Martorell, Sandra; Vellido Alcacena, Alfredo (2013-10-15)
      Article
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      The medical analysis of human brain tumours commonly relies on indirect measurements. Among these, magnetic resonance imaging (MRI) and spectroscopy (MRS) predominate in clinical settings as tools for diagnostic assistance. ...

    • Discriminating glioblastomas from metastases in a SV1H-MRS brain tumour database 

      Romero Merino, Enrique; Vellido Alcacena, Alfredo; Julià Sapé, Margarida; Arús, Carles (2009)
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      A Feature Selection (FS) process with a simple Machine Learning method, namely the Single-Layer Perceptron (SLP), is shown to discriminate metastases from glioblastomas with high accuracy using single voxel H-MRS from an ...