Alzheimer's Disease

Machine Learning Methods for Classifying Multiple Sclerosis and Alzheimer's Disease Using Genomic Data.

Complex diseases pose challenges in prediction due to their multifactorial and polygenic nature. This study employed machine learning (ML) to analyze genomic data from the UK Biobank, aiming to predict the genomic predisposition to complex diseases like multiple sclerosis (MS) and Alzheimer's disease (AD). We tested logistic regression (LR), ensemble tree methods, and deep learning models for this...

Therapeutic Mechanisms of Medicine Food Homology Plants in Alzheimer's Disease: Insights from Network Pharmacology, Machine Learning, and Molecular Docking.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by a gradual decline in cognitive function. Currently, there are no effective treatments for this condition. Medicine food homology plants have gained increasing attention as potential natural treatments for AD because of their nutritional value and therapeutic benefits. In this work, we aimed to provide a deeper un...

Estimation of Machine Learning-Based Models to Predict Dementia Risk in Patients With Atherosclerotic Cardiovascular Diseases: UK Biobank Study.

BACKGROUND: The atherosclerotic cardiovascular disease (ASCVD) is associated with dementia. However, the risk factors of dementia in patients with ASC...

CSEPC: a deep learning framework for classifying small-sample multimodal medical image data in Alzheimer's disease.

BACKGROUND: Alzheimer's disease (AD) is a neurodegenerative disorder that significantly impacts health care worldwide, particularly among the elderly ...

Interpretable modality-specific and interactive graph convolutional network on brain functional and structural connectomes.

Both brain functional connectivity (FC) and structural connectivity (SC) provide distinct neural mechanisms for cognition and neurological disease. In...

Linguistic cues for automatic assessment of Alzheimer's disease across languages.

BackgroundMost common forms of dementia, including Alzheimer's disease, are associated with alterations in spoken language.ObjectiveThis study explore...

Real-World Insights Into Dementia Diagnosis Trajectory and Clinical Practice Patterns Unveiled by Natural Language Processing: Development and Usability Study.

BACKGROUND: Understanding the dementia disease trajectory and clinical practice patterns in outpatient settings is vital for effective management. Kno...

Deep brain stimulation of the nucleus basalis of Meynert in neurodegenerative diseases with cognitive impairment: An update on evidence and mechanisms.

Current pharmacotherapy for neurodegenerative diseases is limited to providing symptomatic relief, instead of slowing or reversing disease progression...

Deep learning to quantify the pace of brain aging in relation to neurocognitive changes.

Brain age (BA), distinct from chronological age (CA), can be estimated from MRIs to evaluate neuroanatomic aging in cognitively normal (CN) individual...

Cognitive performance classification of older patients using machine learning and electronic medical records.

Dementia rates are projected to increase significantly by 2050, posing considerable challenges for healthcare systems worldwide. Developing efficient ...

Identification of UBE2N as a biomarker of Alzheimer's disease by combining WGCNA with machine learning algorithms.

Alzheimer's disease (AD) is the most common cause of dementia, emphasizing the critical need for the development of biomarkers that facilitate accurat...

Hyperfusion: A hypernetwork approach to multimodal integration of tabular and medical imaging data for predictive modeling.

The integration of diverse clinical modalities such as medical imaging and the tabular data extracted from patients' Electronic Health Records (EHRs) ...

Integrating NLP and LLMs to discover biomarkers and mechanisms in Alzheimer's disease.

Alzheimer's disease (AD) is a progressive neurological condition characterized by cognitive decline, memory loss, and aberrant behaviour. It affects m...

Sampling Conformational Ensembles of Highly Dynamic Proteins via Generative Deep Learning.

Proteins are inherently dynamic, and their conformational ensembles play a crucial role in biological function. Large-scale motions may govern the pro...

Enhanced in silico QSAR-based screening of butyrylcholinesterase inhibitors using multi-feature selection and machine learning.

Butyrylcholinesterase inhibition offers one of the formulated solutions to tackle the aggravating symptoms of dementia that downgrades to cholinergic ...

Towards realistic simulation of disease progression in the visual cortex with CNNs.

Convolutional neural networks (CNNs) and mammalian visual systems share architectural and information processing similarities. We leverage these paral...

Artificial intelligence-powered 3D analysis of video-based caregiver-child interactions.

We introduce HARMONI, a three-dimensional (3D) computer vision and audio processing method for analyzing caregiver-child behavior and interaction from...

Identification of biomarkers in Alzheimer's disease and COVID-19 by bioinformatics combining single-cell data analysis and machine learning algorithms.

BACKGROUND: Since its emergence in 2019, COVID-19 has become a global epidemic. Several studies have suggested a link between Alzheimer's disease (AD)...

Stacked CNN-based multichannel attention networks for Alzheimer disease detection.

Alzheimer's Disease (AD) is a progressive condition of a neurological brain disorder recognized by symptoms such as dementia, memory loss, alterations...

Dense convolution-based attention network for Alzheimer's disease classification.

Recently, deep learning-based medical image classification models have made substantial advancements. However, many existing models prioritize perform...