Exploració per autor "Vellido Alcacena, Alfredo"
Ara es mostren els items 116-129 de 129
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The coming of age of interpretable and explainable machine learning models
(I6doc.com, 2021)
Text en actes de congrés
Accés obertMachine learning-based systems are now part of a wide array of real-world applications seamlessly embedded in the social realm. In the wake of this realisation, strict legal regulations for these systems are currently being ... -
The effect of noise and sample size on an unsupervised feature selection method for manifold learning
(IEEE, 2008)
Text en actes de congrés
Accés obertThe research on unsupervised feature selection is scarce in comparison to that for supervised models, despite the fact that this is an important issue for many clustering problems. An unsupervised feature selection method ... -
The extracellular N-terminal domain suffices to discriminate class C G Protein-Coupled Receptor subtypes from n-grams of their sequences
(Institute of Electrical and Electronics Engineers (IEEE), 2015)
Text en actes de congrés
Accés obertThe investigation of protein functionality often relies on the knowledge of crystal 3-D structure. This structure is not always known or easily unravelled, which is the case of eukaryotic cell membrane proteins such as G ... -
The importance of interpretability and visualization in machine learning for applications in medicine and health care
(2020)
Article
Accés obertIn a short period of time, many areas of science have made a sharp transition towards data-dependent methods. In some cases, this process has been enabled by simultaneous advances in data acquisition and the development ... -
The importance of interpretability and visualization in ML for medical applications
(2021)
Comunicació de congrés
Accés obertMany areas of science have made a sharp transition towards data-dependent methods, enabled by simultaneous advances in data acquisition and the development of networked system technologies. This is particularly clear in ... -
The influence of alignment-free sequence representations on the semi-supervised classification of class C G protein-coupled receptors
(2015-02-01)
Article
Accés obert -
Unfolding the Manifold in Generative Topographic Mapping
(2008-09)
Article
Accés restringit per política de l'editorialGenerative Topographic Mapping (GTM) is a probabilistic latent variable model for multivariate data clustering and visualization. It tries to capture the relevant data structure by defining a low-dimensional manifold ... -
Unraveling response to temozolomide in preclinical GL261 glioblastoma with MRI/MRSI using radiomics and signal source extraction
(Nature, 2020-11-12)
Article
Accés obertGlioblastoma is the most frequent aggressive primary brain tumor amongst human adults. Its standard treatment involves chemotherapy, for which the drug temozolomide is a common choice. These are heterogeneous and variable ... -
Using machine learning tools for protein database biocuration assistance
(Nature, 2018-07-05)
Article
Accés obertBiocuration in the omics sciences has become paramount, as research in these fields rapidly evolves towards increasingly data-dependent models. As a result, the management of web-accessible publicly-available databases ... -
Using random forests for assistance in the curation of G-protein coupled receptor databases
(2017-08-18)
Article
Accés obertBackground: Biology is experiencing a gradual but fast transformation from a laboratory-centred science towards a data-centred one. As such, it requires robust data engineering and the use of quantitative data analysis ... -
Using single-voxel magnetic resonance spectroscopy data acquired at 1.5T to classify multivoxel data at 3T: a proof-of-concept study
(Multidisciplinary Digital Publishing Institute (MDPI), 2023-07-01)
Article
Accés obertIn vivo magnetic resonance spectroscopy (MRS) has two modalities, single-voxel (SV) and multivoxel (MV), in which one or more contiguous grids of SVs are acquired. Purpose: To test whether MV grids can be classified with ... -
Variational Bayesian generative topographic mapping
(2008-12)
Article
Accés restringit per política de l'editorialGeneral finite mixture models are powerful tools for the density-based grouping of multivariate i.i.d. data, but they lack data visualization capabilities, which reduces their practical applicability to real-world problems. ... -
Visual characterization of misclassified Class C GPCRs through Manifold-based machine learning methods
(2015-09-18)
Article
Accés obertG-protein-coupled receptors are cell membrane proteins of great interest in biology and pharmacology. Previous analysis of Class C of these receptors has revealed the existence of an upper boundary on the accuracy that can ... -
Visual interpretation of class C GPCR subtype overlapping from the nonlinear mapping of transformed primary sequences
(Institute of Electrical and Electronics Engineers (IEEE), 2014)
Text en actes de congrés
Accés restringit per política de l'editorialThis brief paper addresses the problem of visually assessing the natural discriminability of the different subtypes that characterize class C G-Protein-Coupled Receptors, which are membrane proteins of interest in pharmacology, ...
