AIMC Topic: Alzheimer Disease
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Machine Learning Methods for Classifying Multiple Sclerosis and Alzheimer's Disease Using Genomic Data.
International journal of molecular sciences
Feb 27, 2025
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 l...
CSEPC: a deep learning framework for classifying small-sample multimodal medical image data in Alzheimer's disease.
BMC geriatrics
Feb 26, 2025
BACKGROUND: Alzheimer's disease (AD) is a neurodegenerative disorder that significantly impacts health care worldwide, particularly among the elderly population. The accurate classification of AD stages is essential for slowing disease progression an...
Linguistic cues for automatic assessment of Alzheimer's disease across languages.
Journal of Alzheimer's disease : JAD
Feb 25, 2025
BackgroundMost common forms of dementia, including Alzheimer's disease, are associated with alterations in spoken language.ObjectiveThis study explores the potential of a speech-based machine learning (ML) approach in estimating cognitive impairment,...
Deep learning to quantify the pace of brain aging in relation to neurocognitive changes.
Proceedings of the National Academy of Sciences of the United States of America
Feb 24, 2025
Brain age (BA), distinct from chronological age (CA), can be estimated from MRIs to evaluate neuroanatomic aging in cognitively normal (CN) individuals. BA, however, is a cross-sectional measure that summarizes cumulative neuroanatomic aging since bi...
Identification of UBE2N as a biomarker of Alzheimer's disease by combining WGCNA with machine learning algorithms.
Scientific reports
Feb 22, 2025
Alzheimer's disease (AD) is the most common cause of dementia, emphasizing the critical need for the development of biomarkers that facilitate accurate and objective assessment of disease progression for early detection and intervention to delay its ...
Hyperfusion: A hypernetwork approach to multimodal integration of tabular and medical imaging data for predictive modeling.
Medical image analysis
Feb 21, 2025
The integration of diverse clinical modalities such as medical imaging and the tabular data extracted from patients' Electronic Health Records (EHRs) is a crucial aspect of modern healthcare. Integrative analysis of multiple sources can provide a com...
Integrating NLP and LLMs to discover biomarkers and mechanisms in Alzheimer's disease.
SLAS technology
Feb 21, 2025
Alzheimer's disease (AD) is a progressive neurological condition characterized by cognitive decline, memory loss, and aberrant behaviour. It affects millions of people globally and is one of the main causes of dementia. The neurodegenerative conditio...
Towards realistic simulation of disease progression in the visual cortex with CNNs.
Scientific reports
Feb 19, 2025
Convolutional neural networks (CNNs) and mammalian visual systems share architectural and information processing similarities. We leverage these parallels to develop an in-silico CNN model simulating diseases affecting the visual system. This model a...
IRMA: Machine learning-based harmonization of F-FDG PET brain scans in multi-center studies.
European journal of nuclear medicine and molecular imaging
Feb 18, 2025
PURPOSE: Center-specific effects in PET brain scans arise due to differences in technical and procedural aspects. This restricts the merging of data between centers and introduces source-specific bias.
Identification of biomarkers in Alzheimer's disease and COVID-19 by bioinformatics combining single-cell data analysis and machine learning algorithms.
PloS one
Feb 18, 2025
BACKGROUND: Since its emergence in 2019, COVID-19 has become a global epidemic. Several studies have suggested a link between Alzheimer's disease (AD) and COVID-19. However, there is little research into the mechanisms underlying these phenomena. The...
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