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Alzheimer Disease

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Comparison of machine learning algorithms for automatic prediction of Alzheimer disease.

Journal of the Chinese Medical Association : JCMA
BACKGROUND: Alzheimer disease is a progressive neurological disorder marked by irreversible memory loss and cognitive decline. Traditional diagnostic tools, such as intracranial volume assessments, electroencephalography (EEG) signals, and brain magn...

Identification of CXCR4 inhibitory activity in natural compounds using cheminformatics-guided machine learning algorithms.

Integrative biology : quantitative biosciences from nano to macro
Neurodegenerative disorders are characterised by progressive damage to neurons that leads to cognitive impairment and motor dysfunction. Current treatment options focus only on symptom management and palliative care, without addressing their root cau...

DML-MFCM: A multimodal fine-grained classification model based on deep metric learning for Alzheimer's disease diagnosis.

Journal of X-ray science and technology
BACKGROUND: Alzheimer's disease (AD) is a neurodegenerative disorder. There are no drugs and methods for the treatment of AD, but early intervention can delay the deterioration of the disease. Therefore, the early diagnosis of AD and mild cognitive i...

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

PloS one
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...

Taylor-dingo optimized RP-net for segmentation toward Alzheimer's disease detection and classification using deep learning.

Computational biology and chemistry
Alzheimer's Disease (AD) is a significant cause of mortality in elderly people. The diagnosing and classification of AD using conventional manual operation is a challenging issue. Here, a novel scheme, namely Recurrent Prototypical Network with Taylo...

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

Scientific reports
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 ...

Machine learning prediction of tau-PET in Alzheimer's disease using plasma, MRI, and clinical data.

Alzheimer's & dementia : the journal of the Alzheimer's Association
INTRODUCTION: Tau positron emission tomography (PET) is a reliable neuroimaging technique for assessing regional load of tau pathology in the brain, but its routine clinical use is limited by cost and accessibility barriers.

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

Scientific reports
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...

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

Scientific reports
Recently, deep learning-based medical image classification models have made substantial advancements. However, many existing models prioritize performance at the cost of efficiency, limiting their practicality in clinical use. Traditional Convolution...

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

Scientific reports
Alzheimer's Disease (AD) is a progressive condition of a neurological brain disorder recognized by symptoms such as dementia, memory loss, alterations in behaviour, and impaired reasoning abilities. Recently, many researchers have been working to dev...