Exploració per autor "Vellido Alcacena, Alfredo"
Ara es mostren els items 83-102 de 129
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Machine learning in critical care: state-of-the-art and a sepsis case study
(2018-11-20)
Article
Accés obertBackground: Like other scientific fields, such as cosmology, high-energy physics, or even the life sciences, medicine and healthcare face the challenge of an extremely quick transformation into data-driven sciences. This ... -
Machine learning in human cancer research
(Nova Science Publishers, 2007)
Capítol de llibre
Accés restringit per política de l'editorialEvidence-based medicine has grown in stature over three decades and is now regarded a key development of modern medicine. The evidence base can be heterogeneous, involving both qualitative knowledge and measured quantitative ... -
Making machine learning models interpretable
(2012)
Text en actes de congrés
Accés restringit per política de l'editorial -
Making nonlinear manifold learning models interpretable: the manifold grand tour
(2015-12)
Article
Accés obertDimensionality reduction is required to produce visualisations of high dimensional data. In this framework, one of the most straightforward approaches to visualising high dimensional data is based on reducing complexity ... -
Manifold learning visualization of metabotropic glutamate receptors
(IOS Press, 2014)
Text en actes de congrés
Accés obertG-Protein-Coupled Receptors (GPCRs) are cell membrane proteins with a key role in biological processes. GPCRs of class C, in particular, are of great interest in pharmacology. The lack of knowledge about their 3-D structures ... -
Metrics for probabilistic geometries
(AUAI Press (Association for Uncertainty in Artificial Intelligence), 2014)
Text en actes de congrés
Accés restringit per política de l'editorialWe investigate the geometrical structure of probabilistic generative dimensionality reduction models using the tools of Riemannian geometry. We explicitly define a distribution over the natural metric given by the models. ... -
Misclassification of class C G-protein-coupled receptors as a label noise problem
(2014)
Comunicació de congrés
Accés obertG-Protein-Coupled Receptors (GPCRs) are cell membrane proteins of relevance to biology and pharmacology. Their supervised classification in subtypes is hampered by label noise, which stems from a combination of expert ... -
Missing data imputation through generative topographic mapping as a mixture of t-distributions: Theoretical developments
(2004-11)
Report de recerca
Accés obertThe Generative Topographic Mapping (GTM) was originally conceived as a probabilistic alternative to the well-known, neural network-inspired, Self-Organizing Map (SOM). The GTM can also be interpreted as a constrained mixture ... -
Molecular dynamics forecasting of transmembrane regions in GPRCs by recurrent neural networks
(Institute of Electrical and Electronics Engineers (IEEE), 2022)
Text en actes de congrés
Accés obertG protein-coupled receptors are a large super-family of cell membrane proteins that play an important physiological role as transmitters of extra-cellular signals. Signal transmission through the cell membrane depends on ... -
Network community cluster-based analysis for the identification of potential leukemia drug targets
(Springer, 2019)
Comunicació de congrés
Accés obertLeukemia is a hematologic cancer which develops in blood tissue and causes rapid generation of immature and abnormal-shaped white blood cells. It is one of the most prominent causes of death in both men and women for which ... -
NMF for quality control of multi-modal retinal images for diagnosis of diabetes mellitus and diabetic retinopathy
(Springer, 2022)
Text en actes de congrés
Accés obertIn current ophthalmology, images of the vascular system in the human retina are used as exploratory proxies for pathologies affecting different organs. In this brief paper, we use multi-modal retinal images for assisting ... -
On the benefits for model regularization of a variational formulation of GTM
(IEEE, 2008)
Text en actes de congrés
Accés obertGenerative Topographic Mapping (GTM) is a manifold learning model for the simultaneous visualization and clustering of multivariate data. It was originally formulated as a constrained mixture of distributions, for which ... -
On the computation of the geodesic distance with an application to dimensionality reduction in a neuro-oncology problem
(Springer, 2011)
Text en actes de congrés
Accés restringit per política de l'editorialManifold learning models attempt to parsimoniously describe multivariate data through a low-dimensional manifold embedded in data space. Similarities between points along this manifold are often expressed as Euclidean ... -
On the improvement of the mapping trustworthiness and continuity of a manifold learning model
(2008-11)
Article
Accés restringit per política de l'editorialManifold learningmethodsmodel high-dimensional data through low-dimensional manifolds embedded in the observed data space. This simplification implies that their are prone to trustworthiness and continuity errors. Generative ... -
On the use of graphical models to study ICU outcome prediction in septic patients treated with statins
(Springer, 2012)
Capítol de llibre
Accés restringit per política de l'editorial -
Physics and machine learning: Emerging paradigms
(I6doc.com, 2016)
Text en actes de congrés
Accés obertCurrent research in Machine Learning (ML) combines the study of variations on well-established methods with cutting-edge breakthroughs based on completely new approaches. Among the latter, emerging paradigms from Physics ... -
Predictive models in churn data mining: a review
(2007-01)
Report de recerca
Accés obertThe development of predictive models of customer abandonment plays a central role in any churn management strategy. These models can be developed using either qualitative approaches or can take a data-centred point of view. ... -
Preliminary theoretical results on a feature relevance determination method for Generative Topographic Mapping
(2005-04)
Report de recerca
Accés obertFeature selection (FS) has long been studied in classification and regression problems, following diverse approaches and resulting on a wide variety of methods, usually grouped as either /filters /or /wrappers/. In comparison, ... -
Preprocessing MRS information for classification of human brain tumours
(IGI Global, 2012-06)
Capítol de llibre
Accés restringit per política de l'editorial -
Probability ridges and distortion flows: Visualizing multivariate time series using a variational Bayesian manifold learning method
(Springer, 2014)
Text en actes de congrés
Accés obertTime-dependent natural phenomena and artificial processes can often be quantitatively expressed as multivariate time series (MTS). As in any other process of knowledge extraction from data, the analyst can benefit from the ...
