AIMC Topic: Alzheimer Disease

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Graph Embedded Ensemble Deep Randomized Network for Diagnosis of Alzheimer's Disease.

IEEE/ACM transactions on computational biology and bioinformatics Aug 8, 2024
Randomized shallow/deep neural networks with closed form solution avoid the shortcomings that exist in the back propagation (BP) based trained neural networks. Ensemble deep random vector functional link (edRVFL) network utilize the strength of two g...
Alzheimer Disease Neural Networks, Computer Algorithms Databases, Factual Humans Diagnosis, Computer-Assisted Deep Learning Computational Biology
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A Multi-Classification Accessment Framework for Reproducible Evaluation of Multimodal Learning in Alzheimer's Disease.

IEEE/ACM transactions on computational biology and bioinformatics Aug 8, 2024
Multimodal learning is widely used in automated early diagnosis of Alzheimer's disease. However, the current studies are based on an assumption that different modalities can provide more complementary information to help classify the samples from the...
Brain Alzheimer Disease Image Interpretation, Computer-Assisted Early Diagnosis Neuroimaging Machine Learning Algorithms Databases, Factual Humans
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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 Aug 8, 2024
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...
Diagnosis, Computer-Assisted Deep Learning Computational Biology Neural Networks, Computer Algorithms Alzheimer Disease Humans
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SR-TWAS: leveraging multiple reference panels to improve transcriptome-wide association study power by ensemble machine learning.

Nature communications Aug 5, 2024
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...
Gene Expression Profiling Transcriptome Dementia Humans Machine Learning Parkinson Disease Alzheimer Disease Substantia Nigra Genome-Wide Association Study Genetic Predisposition to Disease
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Fully Automated Hippocampus Segmentation using T2-informed Deep Convolutional Neural Networks.

NeuroImage Aug 3, 2024
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...
Humans Neural Networks, Computer Hippocampus Female Neuroimaging Image Processing, Computer-Assisted Aged Magnetic Resonance Imaging Male Alzheimer Disease Deep Learning
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A novel graph neural network method for Alzheimer's disease classification.

Computers in biology and medicine Aug 2, 2024
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...
Humans Brain Diagnosis, Computer-Assisted Alzheimer Disease Neural Networks, Computer Algorithms Databases, Factual
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Elucidating Microglial Heterogeneity and Functions in Alzheimer's Disease Using Single-cell Analysis and Convolutional Neural Network Disease Model Construction.

Scientific reports Jul 27, 2024
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...
Gene Regulatory Networks Gene Expression Profiling Humans Microglia Neural Networks, Computer Deep Learning Alzheimer Disease Single-Cell Analysis
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Identification of profiles associated with conversions between the Alzheimer's disease stages, using a machine learning approach.

Alzheimer's research & therapy Jul 26, 2024
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 ...
Alzheimer Disease Neuropsychological Tests Aged Aged, 80 and over Severity of Illness Index Male Cognitive Dysfunction Humans Cohort Studies Machine Learning Middle Aged Female Disease Progression
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Exceptional performance with minimal data using a generative adversarial network for alzheimer's disease classification.

Scientific reports Jul 24, 2024
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....
Humans Neural Networks, Computer Databases, Factual Female Male Magnetic Resonance Imaging Deep Learning Alzheimer Disease
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E-BDL: Enhanced Band-Dependent Learning Framework for Augmented Radar Sensing.

Sensors (Basel, Switzerland) Jul 17, 2024
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...
Alzheimer Disease Humans Vital Signs Algorithms Deep Learning Hemodynamics Radar Gait
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