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Alzheimer Disease

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Cross study transcriptomic investigation of Alzheimer's brain tissue discoveries and limitations.

Scientific reports
Developing effective treatments for Alzheimer's disease (AD) likely requires a deep understanding of molecular mechanisms. Integration of transcriptomic datasets and developing innovative computational analyses may yield novel molecular targets with ...

Predicting and Evaluating Cognitive Status in Aging Populations Using Decision Tree Models.

American journal of Alzheimer's disease and other dementias
To improve the identification of cognitive impairment by distinguishing normal cognition (NC), mild cognitive impairment (MCI), and Alzheimer's disease (AD). A recursive partitioning tree model was developed using ARMADA data and the NIH Toolbox, a...

Early detection of Alzheimer's disease using deep learning methods.

Alzheimer's & dementia : the journal of the Alzheimer's Association
INTRODUCTION: Alzheimer's disease (AD), a leading cause of dementia, requires early detection for effective intervention. This study employs AI to analyze multimodal datasets, including clinical, biomarker, and neuroimaging data, using hybrid deep le...

Effective integration of multi-omics with prior knowledge to identify biomarkers via explainable graph neural networks.

NPJ systems biology and applications
The rapid growth of multi-omics datasets and the wealth of biological knowledge necessitates the development of effective methods for their integration. Such methods are essential for building predictive models and identifying drug targets based on a...

EEG-based neurodegenerative disease diagnosis: comparative analysis of conventional methods and deep learning models.

Scientific reports
In the context of lifestyle changes, stress and other environmental factors have resulted in the sudden hike in dementia globally. This necessitates investigations with respect to every horizon of the due cause for it; further on, the diagnosis and t...

Ge-SAND: an explainable deep learning-driven framework for disease risk prediction by uncovering complex genetic interactions in parallel.

BMC genomics
BACKGROUND: Accurate genetic risk prediction and understanding the mechanisms underlying complex diseases are essential for effective intervention and precision medicine. However, current methods often struggle to capture the intricate and subtle gen...

Early detection of Alzheimer's disease progression stages using hybrid of CNN and transformer encoder models.

Scientific reports
Alzheimer's disease (AD) is a neurodegenerative disorder that affects memory and cognitive functions. Manual diagnosis is prone to human error, often leading to misdiagnosis or delayed detection. MRI techniques help visualize the fine tissues of the ...

Deep normative modelling reveals insights into early-stage Alzheimer's disease using multi-modal neuroimaging data.

Alzheimer's research & therapy
BACKGROUND: Exploring the early stages of Alzheimer's disease (AD) is crucial for timely intervention to help manage symptoms and set expectations for affected individuals and their families. However, the study of the early stages of AD involves anal...

An integrated deep learning model for early and multi-class diagnosis of Alzheimer's disease from MRI scans.

Scientific reports
Alzheimer's disease (AD) is a progressive neurodegenerative disorder that severely affects memory, behavior, and cognitive function. Early and accurate diagnosis is crucial for effective intervention, yet detecting subtle changes in the early stages ...

Translational approach for dementia subtype classification using convolutional neural network based on EEG connectome dynamics.

Scientific reports
Dementia spectrum disorders, characterized by progressive cognitive decline, pose a significant global health burden. Early screening and diagnosis are essential for timely and accurate treatment, improving patient outcomes and quality of life. This ...