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Alzheimer Disease

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Ensemble Deep Random Vector Functional Link Network Using Privileged Information for Alzheimer's Disease Diagnosis.

IEEE/ACM transactions on computational biology and bioinformatics
Alzheimer's disease (AD) is a progressive brain disorder. Machine learning models have been proposed for the diagnosis of AD at early stage. Recently, deep learning architectures have received quite a lot attention. Most of the deep learning architec...

SR-TWAS: leveraging multiple reference panels to improve transcriptome-wide association study power by ensemble machine learning.

Nature communications
Multiple reference panels of a given tissue or multiple tissues often exist, and multiple regression methods could be used for training gene expression imputation models for transcriptome-wide association studies (TWAS). To leverage expression imputa...

Fully Automated Hippocampus Segmentation using T2-informed Deep Convolutional Neural Networks.

NeuroImage
Hippocampal atrophy (tissue loss) has become a fundamental outcome parameter in clinical trials on Alzheimer's disease. To accurately estimate hippocampus volume and track its volume loss, a robust and reliable segmentation is essential. Manual hippo...

A novel graph neural network method for Alzheimer's disease classification.

Computers in biology and medicine
Alzheimer's disease (AD) is a chronic neurodegenerative disease. Early diagnosis are very important to timely treatment and delay the progression of the disease. In the past decade, many computer-aided diagnostic (CAD) algorithms have been proposed f...

Elucidating Microglial Heterogeneity and Functions in Alzheimer's Disease Using Single-cell Analysis and Convolutional Neural Network Disease Model Construction.

Scientific reports
In this study, we conducted an in-depth exploration of Alzheimer's Disease (AD) by integrating state-of-the-art methodologies, including single-cell RNA sequencing (scRNA-seq), weighted gene co-expression network analysis (WGCNA), and a convolutional...

Identification of profiles associated with conversions between the Alzheimer's disease stages, using a machine learning approach.

Alzheimer's research & therapy
BACKGROUND: The identification of factors involved in the conversion across the different Alzheimer's disease (AD) stages is crucial to prevent or slow the disease progression. We aimed to assess the factors and their combination associated with the ...

Exceptional performance with minimal data using a generative adversarial network for alzheimer's disease classification.

Scientific reports
The classification of Alzheimer's disease (AD) using deep learning models is hindered by the limited availability of data. Medical image datasets are scarce due to stringent regulations on patient privacy, preventing their widespread use in research....

E-BDL: Enhanced Band-Dependent Learning Framework for Augmented Radar Sensing.

Sensors (Basel, Switzerland)
Radar sensors, leveraging the Doppler effect, enable the nonintrusive capture of kinetic and physiological motions while preserving privacy. Deep learning (DL) facilitates radar sensing for healthcare applications such as gait recognition and vital-s...

XAI-Based Assessment of the AMURA Model for Detecting Amyloid-β and Tau Microstructural Signatures in Alzheimer's Disease.

IEEE journal of translational engineering in health and medicine
Brain microstructural changes already occur in the earliest phases of Alzheimer's disease (AD) as evidenced in diffusion magnetic resonance imaging (dMRI) literature. This study investigates the potential of the novel dMRI Apparent Measures Using Red...

Disentangling brain atrophy heterogeneity in Alzheimer's disease: A deep self-supervised approach with interpretable latent space.

NeuroImage
Alzheimer's disease (AD) is heterogeneous, but existing methods for capturing this heterogeneity through dimensionality reduction and unsupervised clustering have limitations when it comes to extracting intricate atrophy patterns. In this study, we p...