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A Cascading Machine Learning-SHAP Framework for the Multimodal Characterisation of Mild Cognitive Impairment and Dementia Due to Alzheimer's Disease
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Outgoing claims: the papers this work cites, builds on, or argues with.
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Mild cognitive impairment and Alzheimer's disease
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The Elements of Statistical Learning: Data Mining, Inference, and Prediction
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Alpha and Theta Rhythm Abnormality in Alzheimer’s Disease: A Study Using a Computational Model
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Cortical hypermetabolism in MCI subjects: a compensatory mechanism?
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SORL1 genetic variants and Alzheimer disease risk: a literature review and meta-analysis of sequencing data
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Regional Analysis of Spontaneous MEG Rhythms in Patients with Alzheimer’s Disease Using Spectral Entropies
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The Role of MAPT in Neurodegenerative Diseases: Genetics, Mechanisms and Therapy
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Late-Onset Alzheimer’s Disease Genes and the Potentially Implicated Pathways
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Integrative and interpretable framework to unveil the neurophysiological fingerprint of Alzheimer’s disease and mild cognitive impairment: A machine learning-SHAP approach
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A candidate gene of Alzheimer diseases was mutated in senescence-accelerated mouse prone (SAMP) 8 mice
inferredcites →https://doi.org/10.1016/j.biopsych.2014.05.006openalex-snapshot
Alzheimer’s Disease Risk Genes and Mechanisms of Disease Pathogenesis
inferredcites →https://doi.org/10.1016/j.biopsych.2017.05.014openalex-snapshot
Untangling Genetic Risk for Alzheimer’s Disease
inferredcites →https://doi.org/10.1016/j.brainres.2016.03.018openalex-snapshot
Alzheimer's disease genes and autophagy
inferredcites →https://doi.org/10.1016/j.camwa.2006.12.098openalex-snapshot
A multilevel tabu search algorithm for the feature selection problem in biomedical data
inferredcites →https://doi.org/10.1016/j.clinph.2005.06.011openalex-snapshot
Nonlinear dynamical analysis of EEG and MEG: Review of an emerging field
inferredcites →https://doi.org/10.1016/j.clinph.2012.01.011openalex-snapshot
The organization of physiological brain networks
inferredcites →https://doi.org/10.1016/j.clinph.2017.06.251openalex-snapshot
Feature selection before EEG classification supports the diagnosis of Alzheimer’s disease
inferredcites →https://doi.org/10.1016/j.clinph.2020.03.003openalex-snapshot
Early diagnosis of Alzheimer’s disease: the role of biomarkers including advanced EEG signal analysis. Report from the IFCN-sponsored panel of experts
inferredcites →https://doi.org/10.1016/j.cmpb.2007.07.001openalex-snapshot
Analysis of the magnetoencephalogram background activity in Alzheimer's disease patients with auto-mutual information
inferredcites →https://doi.org/10.1016/j.compbiomed.2009.08.001openalex-snapshot
A note on fractal dimensions of biomedical waveforms
inferredcites →https://doi.org/10.1016/j.jalz.2011.03.005openalex-snapshot
The diagnosis of dementia due to Alzheimer's disease: Recommendations from the National Institute on Aging‐Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease
inferredcites →https://doi.org/10.1016/j.jalz.2011.03.008openalex-snapshot
The diagnosis of mild cognitive impairment due to Alzheimer's disease: Recommendations from the National Institute on Aging‐Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease
inferredcites →https://doi.org/10.1016/j.jalz.2016.05.009openalex-snapshot
APOE and MS4A6A interact with GnRH signaling in Alzheimer's disease: Enrichment of epistatic effects
inferredcites →https://doi.org/10.1016/j.jalz.2018.02.018openalex-snapshot
NIA‐AA Research Framework: Toward a biological definition of Alzheimer's disease
inferredcites →https://doi.org/10.1016/j.jneumeth.2017.12.005openalex-snapshot
Ensemble of random forests One vs. Rest classifiers for MCI and AD prediction using ANOVA cortical and subcortical feature selection and partial least squares
inferredcites →https://doi.org/10.1016/j.jsbmb.2016.03.012openalex-snapshot
Age, APOE and sex: Triad of risk of Alzheimer’s disease
inferredcites →https://doi.org/10.1016/j.medengphy.2005.07.004openalex-snapshot
Analysis of EEG background activity in Alzheimer's disease patients with Lempel–Ziv complexity and central tendency measure
inferredcites →https://doi.org/10.1016/j.medengphy.2006.11.006openalex-snapshot
Extraction of spectral based measures from MEG background oscillations in Alzheimer's disease
inferredcites →https://doi.org/10.1016/j.medengphy.2008.06.010openalex-snapshot
Use of the Higuchi's fractal dimension for the analysis of MEG recordings from Alzheimer's disease patients
inferredcites →https://doi.org/10.1016/j.medengphy.2015.02.006openalex-snapshot
MEG analysis of neural dynamics in attention-deficit/hyperactivity disorder with fuzzy entropy
inferredcites →https://doi.org/10.1016/j.neulet.2014.01.004openalex-snapshot
Decreased expression of CD33 in peripheral mononuclear cells of Alzheimer's disease patients
inferredcites →https://doi.org/10.1016/j.neurobiolaging.2013.02.020openalex-snapshot
Alzheimer's disease: connecting findings from graph theoretical studies of brain networks
inferredcites →https://doi.org/10.1016/j.neurobiolaging.2013.08.002openalex-snapshot
Alzheimer's disease susceptibility variants in the MS4A6A gene are associated with altered levels of MS4A6A expression in blood
inferredcites →https://doi.org/10.1016/j.neuroimage.2006.01.021openalex-snapshot
An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest
inferredcites →https://doi.org/10.1016/j.neuroimage.2009.10.003openalex-snapshot
Complex network measures of brain connectivity: Uses and interpretations
inferredcites →https://doi.org/10.1016/j.neuroimage.2010.11.004openalex-snapshot
Introduction to machine learning for brain imaging
inferredcites →https://doi.org/10.1016/j.neuroimage.2011.11.055openalex-snapshot
Disrupted modular brain dynamics reflect cognitive dysfunction in Alzheimer's disease
inferredcites →https://doi.org/10.1016/j.neuroimage.2017.10.003openalex-snapshot
Dynamics of large-scale electrophysiological networks: A technical review
inferredcites →https://doi.org/10.1016/j.neuron.2010.10.013openalex-snapshot
The Genetics of Alzheimer Disease: Back to the Future
inferredcites →https://doi.org/10.1016/j.neuron.2021.01.023openalex-snapshot
Behavior needs neural variability
inferredcites →https://doi.org/10.1016/j.patcog.2015.03.009openalex-snapshot
Performance evaluation of classification algorithms by k-fold and leave-one-out cross validation
inferredcites →https://doi.org/10.1016/j.semcdb.2021.08.008openalex-snapshot
Assigning function to SNPs: Considerations when interpreting genetic variation
inferredcites →https://doi.org/10.1016/s0074-7742(09)86005-4openalex-snapshot
Chapter 5 Fundamentals of Electroencefalography, Magnetoencefalography, and Functional Magnetic Resonance Imaging
inferredcites →https://doi.org/10.1038/ng.439openalex-snapshot
Genome-wide association study identifies variants at CLU and CR1 associated with Alzheimer's disease
inferredcites →https://doi.org/10.1038/ng.440openalex-snapshot
Genome-wide association study identifies variants at CLU and PICALM associated with Alzheimer's disease
inferredcites →https://doi.org/10.1038/s41598-021-99589-yopenalex-snapshot
Influence of PICALM and CLU risk variants on beta EEG activity in Alzheimer’s disease patients
inferredcites →https://doi.org/10.1038/s42256-019-0138-9openalex-snapshot
From local explanations to global understanding with explainable AI for trees
inferredcites →https://doi.org/10.1093/brain/awn262openalex-snapshot
Graph theoretical analysis of magnetoencephalographic functional connectivity in Alzheimer's disease
inferredcites →https://doi.org/10.1093/brain/awx050openalex-snapshot
Selective impairment of hippocampus and posterior hub areas in Alzheimer’s disease: an MEG-based multiplex network study
inferredcites →https://doi.org/10.1142/s0129065717500320openalex-snapshot
Quantification of Graph Complexity Based on the Edge Weight Distribution Balance: Application to Brain Networks
inferredcites →https://doi.org/10.1177/001316446002000104openalex-snapshot
A Coefficient of Agreement for Nominal Scales
inferredcites →https://doi.org/10.1177/1536867x20909688openalex-snapshot
The random forest algorithm for statistical learning
inferredcites →https://doi.org/10.1177/155005941104200304openalex-snapshot
Improving Alzheimer's Disease Diagnosis with Machine Learning Techniques
inferredcites →https://doi.org/10.1371/journal.pone.0130419openalex-snapshot
Effect of APOE and CD33 on Cognitive Decline
inferredcites →https://doi.org/10.1371/journal.pone.0219569openalex-snapshot
Validity of screening instruments for the detection of dementia and mild cognitive impairment in hospital inpatients: A systematic review of diagnostic accuracy studies
inferredcites →https://doi.org/10.14283/jpad.2021.23openalex-snapshot
Diagnosis of Early Alzheimer's Disease: Clinical Practice in 2021
inferredcites →https://doi.org/10.21037/qims-21-430openalex-snapshot
Fully automated discrimination of Alzheimer’s disease using resting-state electroencephalography signals
inferredcites →https://doi.org/10.3233/jad-179921openalex-snapshot
Multifactorial Hypothesis and Multi-Targets for Alzheimer’s Disease
inferredcites →https://doi.org/10.3389/fnagi.2015.00031openalex-snapshot
Theta and alpha EEG frequency interplay in subjects with mild cognitive impairment: evidence from EEG, MRI, and SPECT brain modifications
inferredcites →https://doi.org/10.3389/fnbot.2013.00021openalex-snapshot
Gradient boosting machines, a tutorial
inferredcites →https://doi.org/10.3389/fncel.2020.00282openalex-snapshot
Alcohol Use Disorder, Neurodegeneration, Alzheimer’s and Parkinson’s Disease: Interplay Between Oxidative Stress, Neuroimmune Response and Excitotoxicity
inferredcites →https://doi.org/10.3390/diagnostics11081437openalex-snapshot
Alzheimer’s Disease and Frontotemporal Dementia: A Robust Classification Method of EEG Signals and a Comparison of Validation Methods
inferredcites →https://doi.org/10.4103/ijam.ijam_92_17openalex-snapshot
Type I, II, and III statistical errors: A brief overview
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