Browsing by Author "Vellido Alcacena, Alfredo"
Now showing items 1-20 of 129
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A comparison of non-negative matrix underapproximation methods for the decomposition of magnetic resonance spectroscopy data from human brain tumors
(John Wiley & sons, 2023-12)
Article
Open AccessMagnetic resonance spectroscopy (MRS) is an MR technique that provides informa-tion about the biochemistry of tissues in a noninvasive way. MRS has been widelyused for the study of brain tumors, both preoperatively and ... -
A decision making support tool: The resilience management fuzzy controller
(Institute of Electrical and Electronics Engineers (IEEE), 2017)
Conference report
Open AccessIn 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 ... -
A deep learning-based method for uncovering GPCR ligand-induced conformational states using interpretability techniques
(Springer, 2022)
Conference report
Open AccessThere is increasing interest in the development of tools for investigating the protein ligand space. Understanding the underlying mechanisms of G protein-coupled receptors (GPCR) in the ligand-binding process is of particular ... -
A machine learning pipeline for supporting differentiation of glioblastomas from single brain metastases
(I6doc.com, 2016)
Conference report
Open AccessMachine 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
(2014)
Conference report
Restricted access - publisher's policyConvex 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
(Institute of Electrical and Electronics Engineers (IEEE), 2017)
Conference lecture
Open AccessAlgorithmic 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
(2011)
Conference report
Open AccessThe 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
(2013)
Conference report
Open AccessIn 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. ... -
A self-organizing world: special issue of the 13th edition of the workshop on self-organizing maps and learning vector quantization, clustering and data visualization, WSOM¿+¿2019
(Springer Nature, 2022-01)
Article
Open Access -
A variational Bayesian formulation for GTM: Theoretical foundations
(2007-09)
Research report
Open AccessGenerative 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
(IEEE, 2008)
Conference report
Open AccessGenerative 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
(2007-10)
Research report
Open AccessGenerative 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 Cramer's V index for the assessment of stability in the fuzzy clustering of class C G protein-coupled receptors
(Springer, 2015)
Part of book or chapter of book
Open AccessAfter 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 Cramér’s V Index for the assessment of stability in the fuzzy clustering of class C G protein-coupled receptors
(Springer, 2015)
Conference report
Open AccessAfter 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 ... -
Advances in clustering and visualization of time series using GTM through time
(2008-09)
Article
Restricted access - publisher's policyMost 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
(2009-03)
Article
Restricted access - publisher's policy -
Advances in semi-supervised alignment-free classification of G protein-coupled receptors
(2013)
Conference report
Open AccessG 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 ... -
Advances in the use of deep learning for the analysis of magnetic resonance image in neuro-oncology
(Multidisciplinary Digital Publishing Institute (MDPI), 2024-01-10)
Article
Open AccessMachine Learning is entering a phase of maturity, but its medical applications still lag behind in terms of practical use. The field of oncological radiology (and neuro-oncology in particular) is at the forefront of these ... -
AI-based glioma grading for a trustworthy diagnosis: an analytical pipeline for improved reliability
(2023-06-27)
Article
Open AccessGlioma is the most common type of tumor in humans originating in the brain. According to the World Health Organization, gliomas can be graded on a four-stage scale, ranging from the most benign to the most malignant. The ... -
Artificial intelligence for the artificial kidney: Pointers to the future of a personalized hemodialysis therapy
(2018-02)
Article
Open AccessCurrent 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 ...
