Neuropsychological predictors of conversion from mild cognitive impairment to Alzheimer’s disease: a feature selection ensemble combining stability and predictability

Background Predicting progression from Mild Cognitive Impairment (MCI) to Alzheimer’s Disease (AD) is an utmost open issue in AD-related research. Neuropsychological assessment has proven to be useful in identifying MCI patients who are likely to convert to dementia. However, the large battery of neuropsychological tests (NPTs) performed in clinical practice and the limited number of training examples are challenge to machine learning when learning prognostic models. In this context, it is paramount to pursue approaches that effectively seek for reduced sets of relevant features. Subsets of NPTs from which prognostic models can be learnt should not only be good predictors, but also stable, promoting generalizable and explainable models. Methods We propose a feature selection (FS) ensemble combining stability and predictability to choose the most relevant NPTs for prognostic prediction in AD. First, we combine the outcome of multiple (filter and embedded) FS methods. Then, we use a wrapper-based approach optimizing both stability and predictability to compute the number of selected features. We use two large prospective studies (ADNI and the Portuguese Cognitive Complaints Cohort, CCC) to evaluate the approach and assess the predictive value of a large number of NPTs. Results The best subsets of features include approximately 30 and 20 (from the original 79 and 40) features, for ADNI and CCC data, respectively, yielding stability above 0.89 and 0.95, and AUC above 0.87 and 0.82. Most NPTs learnt using the proposed feature selection ensemble have been identified in the literature as strong predictors of conversion from MCI to AD. Conclusions The FS ensemble approach was able to 1) identify subsets of stable and relevant predictors from a consensus of multiple FS methods using baseline NPTs and 2) learn reliable prognostic models of conversion from MCI to AD using these subsets of features. The machine learning models learnt from these features outperformed the models trained without FS and achieved competitive results when compared to commonly used FS algorithms. Furthermore, the selected features are derived from a consensus of methods thus being more robust, while releasing users from choosing the most appropriate FS method to be used in their classification task.PTDC/EEI-SII/1937/2014; SFRH/BD/95846/2013; SFRH/BD/118872/2016info:eu-repo/semantics/publishedVersio

A Review of Feature Reduction Methods for QSAR-Based Toxicity Prediction

Thousands of molecular descriptors (1D to 4D) can be generated and used as features to model quantitative structure–activity or toxicity relationship (QSAR or QSTR) for chemical toxicity prediction. This often results in models that suffer from the “curse of dimensionality”, a problem that can occur in machine learning practice when too many features are employed to train a model. Here we discuss different methods of eliminating redundant and irrelevant features to enhance prediction performance, increase interpretability, and reduce computational complexity. Several feature selection and extraction methods are summarized along with their strengths and shortcomings. We also highlight some commonly overlooked challenges such as algorithm instability and selection bias while offering possible solutions

[Correspondencia de Camilo Díaz Baliño] , 1917-1936

Mss. (algúns en fotocopia) autógrafo e mecanografiadoResumen: Correspondencia recibida por Camilo Díaz Baliño entre os anos 1917-1936 relacionada con asuntos persoais e laboraisBiblioteca de GaliciaForma de ingreso: Depósito. Fuente de ingreso: Díaz Pardo, Isaac. Fecha de ingreso: 2011. Propietario: Herdeiros de Isaac Díaz PardoDixitalización Telefónica-IDP 2012Contén : Cartas de: Manuel Abelenda (1 páx.) -- Cesar Alvarez (1 páx.) -- Carlos Amigo Collía (2 páxs.) -- Banco Hispano-Americano (2 páxs.) -- Alfonso Barreiro (3 páxs.) -- Eliseo Barros Gamallo (1 páx.),(2 páxs.) -- Ramón Beade (2 páxs.) -- Benito(2 páxs.) -- Fernando Blanco(1 páx.) -- José Bouzas y Cardama (1 páx.) -- Albino Bouzó Fernández (1 páx.),(2 páxs) -- José Cabada Vázquez (4 páxs.),(1 páx.),(1 páx.) --Salvador Cabeza (1 páx.) -- Antonio Carballa (1 páx.) -- Leandro y Euxenio Carré (2 páxs.) -- V. Carro (1 páx.) -- Santiago Casares (1 páx.) -- Alvaro Cebreiro (2 páxs.) -- Centro Gallego de Buenos Aires (1 páx.),(1 páx.) -- Compostela (2 páxs.) -- Manolo: Continental (2 páxs.) -- Coral de Ruada (1 páx.),(2 páxs.) -- Amando Cotarelo(1 páx.),(1 páx.) -- Eduardo Dorado Xaneiro (8 páx.) -- Círculo Mercantil e Idustrial: Ramón Fernández (1 páx.) --Virgilio Fernández(3 páxs.) -- Ramón Fernández Mato (2 páxs.) -- B. Ferreiro(1 páx.) -- Jenaro de la Fuente (1 páx.) -- Isaac Fraga: Espéctaculos Empresa Fraga (1 páx.),(1 páx.) -- Antonio Folgar Lema(1 páx.)--Alicio Garcitoral (1 páx.) -- Cándido González Raño (1 páx.) -- Daniel González Rodriguez (2 páxs.),(2 páxs.) -- Edurardo G.del Río (1 páx.) -- Hermanos Hernández (2 páxs.),(1 páx.),(1 páx.),(1 páx.) -- José Iglesias Sánchez (2 páxs.) -- Irmandades da Fala (1 páx.) -- José Silva? (2 páxs.) -- Arturo Longa (1 páx.) -- Casimiro López (1 páx.) -- Edmundo López (1 páx.),(1 páx.) -- Eduardo R. Losada y Rebellón (2 páx.) -- Carlos Maside (1 páx.) -- Enrique Mayer (1 páx.) -- Antonio Méndez Laserna (1 páx.) -- Anselmo Padín (1 páx.) -- Xavier Pardo (1 páx.) -- Partido Republicano Radical Socialista (1 páx.) -- Pérez Bustamante (1 páx.) -- Modesto Piñeiro (2 páxs.) -- Salustiano Portela (2 páxs.) -- José Seijo Rubio (2 páxs.) -- Suarez Picallo (2 páxs.) -- Luis Losada (1 páx), (1 páx.) -- Ricardo Valdés (2 páxs.),(2 páxs.),(2 páxs.),(1 páx.) -- A.Nilo Varela (1 páx.),(2 páxs.),(2 páxs.) -- Juan Varela de Limia (1 páx.) -- Victorino? Varela (1 páx.) -- Jesús Varela (3 páxs.) -- F.Vázquez Suarez (1 páx.) -- Santiago Vidal Gimeno (1 páx.) -- Pedro Vieitez (1 páx.) -- M. Villar (2 páxs.) -- Anónima (1 páx.) -- Anónima (1 páx.

Paleoflora y Paleovegetación Ibérica III: Holoceno

International audienc