AIMC Topic: Alzheimer Disease

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

SLAS technology
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
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
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
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...

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...

Graph and Multi-Level Sequence Fusion Learning for Predicting the Molecular Activity of BACE-1 Inhibitors.

International journal of molecular sciences
The development of BACE-1 (β-site amyloid precursor protein cleaving enzyme 1) inhibitors is a crucial focus in exploring early treatments for Alzheimer's disease (AD). Recently, graph neural networks (GNNs) have demonstrated significant advantages i...

Insights from the eyes: a systematic review and meta-analysis of the intersection between eye-tracking and artificial intelligence in dementia.

Aging & mental health
OBJECTIVES: Dementia can change oculomotor behavior, which is detectable through eye-tracking. This study aims to systematically review and conduct a meta-analysis of current literature on the intersection between eye-tracking and artificial intellig...

Exploring Potential Medications for Alzheimer's Disease with Psychosis by Integrating Drug Target Information into Deep Learning Models: A Data-Driven Approach.

International journal of molecular sciences
Approximately 50% of Alzheimer's disease (AD) patients develop psychotic symptoms, leading to a subtype known as psychosis in AD (AD + P), which is associated with accelerated cognitive decline compared to AD without psychosis. Currently, no FDA-appr...

Reducing inference cost of Alzheimer's disease identification using an uncertainty-aware ensemble of uni-modal and multi-modal learners.

Scientific reports
While multi-modal deep learning approaches trained using magnetic resonance imaging (MRI) and fluorodeoxyglucose positron emission tomography (FDG PET) data have shown promise in the accurate identification of Alzheimer's disease, their clinical appl...