Voxel-Based Diagnosis of Alzheimer's Disease Using Classifier Ensembles
暂无分享,去创建一个
Rubén Armañanzas | Martina Iglesias | Dinora A. Morales | Lidia Alonso-Nanclares | L. Alonso-Nanclares | D. Morales | R. Armañanzas | M. Iglesias
[1] Concha Bielza,et al. Peakbin Selection in Mass Spectrometry Data Using a Consensus Approach with Estimation of Distribution Algorithms , 2011, IEEE/ACM Transactions on Computational Biology and Bioinformatics.
[2] Stephan Bandelow,et al. Visual impairment in Alzheimer's disease: a critical review. , 2010, Journal of Alzheimer's disease : JAD.
[3] Catie Chang,et al. Applications of Multivariate Pattern Classification Analyses in Developmental Neuroimaging of Healthy and Clinical Populations , 2009, Front. Hum. Neurosci..
[4] Constantin F. Aliferis,et al. A comprehensive evaluation of multicategory classification methods for microarray gene expression cancer diagnosis , 2004, Bioinform..
[5] María Concepción Bielza Lozoya,et al. Ensemble transcript interaction networks: A case study on Alzheimer's disease , 2012 .
[6] Alzheimer’s Association. 2015 Alzheimer's disease facts and figures , 2015, Alzheimer's & Dementia.
[7] Daoqiang Zhang,et al. Multi-modal multi-task learning for joint prediction of multiple regression and classification variables in Alzheimer's disease , 2012, NeuroImage.
[8] Rubén Armañanzas. Consensus Policies to Solve Bioinformatic Problems , 2012 .
[9] R. Kraftsik,et al. Primary motor cortex involvement in Alzheimer disease. , 1999, Journal of neuropathology and experimental neurology.
[10] C. Bielza,et al. Machine Learning Approach for the Outcome Prediction of Temporal Lobe Epilepsy Surgery , 2013, PloS one.
[11] Juan José Rodríguez Diez,et al. Classifier Ensembles with a Random Linear Oracle , 2007, IEEE Transactions on Knowledge and Data Engineering.
[12] John H. Holland,et al. Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .
[13] Pat Langley,et al. Estimating Continuous Distributions in Bayesian Classifiers , 1995, UAI.
[14] Paolo Maria Rossini,et al. Motor cortex excitability in Alzheimer's disease: A transcranial magnetic stimulation study , 2003, Annals of neurology.
[15] Ron Kohavi,et al. Wrappers for Feature Subset Selection , 1997, Artif. Intell..
[16] Tin Kam Ho,et al. The Random Subspace Method for Constructing Decision Forests , 1998, IEEE Trans. Pattern Anal. Mach. Intell..
[17] Marko Robnik-Sikonja,et al. Overcoming the Myopia of Inductive Learning Algorithms with RELIEFF , 2004, Applied Intelligence.
[18] Hiroshi Motoda,et al. Computational Methods of Feature Selection , 2022 .
[19] Xinghua Lu,et al. Feature selection for fMRI-based deception detection , 2009, BMC Bioinformatics.
[20] Ludmila I Kuncheva,et al. Classifier ensembles for fMRI data analysis: an experiment. , 2010, Magnetic resonance imaging.
[21] H. Braak,et al. Neuropathological stageing of Alzheimer-related changes , 2004, Acta Neuropathologica.
[22] Karl J. Friston,et al. Statistical parametric mapping (SPM) , 2008, Scholarpedia.
[23] Marcos Ríos-Lago,et al. Grapheme-color synesthetes show peculiarities in their emotional brain: cortical and subcortical evidence from VBM analysis of 3D-T1 and DTI data , 2013, Experimental Brain Research.
[24] Concha Bielza,et al. Machine Learning in Bioinformatics , 2008, Encyclopedia of Database Systems.
[25] Uma Shahani,et al. Reduced Haemodynamic Response in the Ageing Visual Cortex Measured by Absolute fNIRS , 2015, PloS one.
[26] Pablo Martínez-Martín,et al. Alzheimer Center Reina Sofia Foundation: fighting the disease and providing overall solutions. , 2010, Journal of Alzheimer's disease : JAD.
[27] S. Kastner,et al. Complex organization of human primary motor cortex: a high-resolution fMRI study. , 2008, Journal of neurophysiology.
[28] Terry M. Peters,et al. 3D statistical neuroanatomical models from 305 MRI volumes , 1993, 1993 IEEE Conference Record Nuclear Science Symposium and Medical Imaging Conference.
[29] Clifford R Jack,et al. Alzheimer disease: new concepts on its neurobiology and the clinical role imaging will play. , 2012, Radiology.
[30] Chih-Jen Lin,et al. LIBLINEAR: A Library for Large Linear Classification , 2008, J. Mach. Learn. Res..
[31] Leo Breiman,et al. Random Forests , 2001, Machine Learning.
[32] John C. Platt,et al. Fast training of support vector machines using sequential minimal optimization, advances in kernel methods , 1999 .
[33] Dimitrios I. Fotiadis,et al. A six stage approach for the diagnosis of the Alzheimer's disease based on fMRI data , 2010, J. Biomed. Informatics.
[34] J. Morris,et al. Functional Brain Imaging of Young, Nondemented, and Demented Older Adults , 2000, Journal of Cognitive Neuroscience.
[35] Pedro Larrañaga,et al. Identification of a biomarker panel for colorectal cancer diagnosis , 2012, BMC Cancer.
[36] Nick C Fox,et al. Automatic classification of MR scans in Alzheimer's disease. , 2008, Brain : a journal of neurology.
[37] Andrea Bergmann,et al. Statistical Parametric Mapping The Analysis Of Functional Brain Images , 2016 .
[38] Jason Weston,et al. Gene Selection for Cancer Classification using Support Vector Machines , 2002, Machine Learning.
[39] F. Vogel,et al. The limbic system in Alzheimer's disease. A neuropathologic investigation. , 1976, The American journal of pathology.
[40] Concha Bielza,et al. Unveiling relevant non-motor Parkinson's disease severity symptoms using a machine learning approach , 2013, Artif. Intell. Medicine.
[41] Karl J. Friston,et al. Statistical parametric mapping , 2013 .
[42] Pedro Larrañaga,et al. Microarray Analysis of Autoimmune Diseases by Machine Learning Procedures , 2009, IEEE Transactions on Information Technology in Biomedicine.