AIMC Topic: Alzheimer Disease

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Multi-tissue Methylation Analysis of Alzheimer's Disease: Insights into Pathways, Modules, and Key Genes.

Journal of molecular neuroscience : MN
DNA methylation plays a crucial role in the onset and progression of Alzheimer's disease (AD). Genome-wide methylation analysis of multi-tissue data can provide insights into the pathology and diagnostic biomarkers of AD. Computational tools were emp...

Integrated machine learning and deep learning-based virtual screening framework identifies novel natural GSK-3β inhibitors for Alzheimer's disease.

Journal of computer-aided molecular design
Alzheimer's disease (AD) is a progressive neurodegenerative disorder lacking effective therapies. Glycogen synthase kinase-3β (GSK-3β), a key regulator of Aβ aggregation and Tau hyperphosphorylation, has emerged as a promising therapeutic target. Her...

Future of Alzheimer's detection: Advancing diagnostic accuracy through the integration of qEEG and artificial intelligence.

NeuroImage
This comprehensive review examines the integration of Quantitative Electroencephalography (qEEG) and Artificial Intelligence (AI) in the detection and diagnosis of Alzheimer's Disease (AD). Through systematic analysis of 11 key studies across multipl...

ML-based prediction to experimental validation: Development of dihydroquinazoline based multi-potent ligands as anti-Alzheimer's agents.

Computers in biology and medicine
Alzheimer's disease (AD) is a multifactorial neurological disorder accounting for the cognitive decline in the patients. The disease is linked to numerous pathological factors including hyperactivation of acetylcholinesterase (AChE) and monoamine oxi...

A hybrid learning approach for MRI-based detection of alzheimer's disease stages using dual CNNs and ensemble classifier.

Scientific reports
Alzheimer's Disease (AD) and related dementias are significant global health issues characterized by progressive cognitive decline and memory loss. Computer-aided systems can help physicians in the early and accurate detection of AD, enabling timely ...

Fusion of bio-inspired optimization and machine learning for Alzheimer's biomarker analysis.

Computers in biology and medicine
Identification of Alzheimer's Disease (AD), especially in its early phases, presents significant challenges due to the nonexistence of reliable biomarkers and effective treatments. Clinical trials for AD medications also suffer from high failure rate...

Rapid Fluorescence Lifetime Imaging through One-Dimensional Deep Learning Optimization.

Analytical chemistry
Traditional fluorescence lifetime imaging (FLIM) provides valuable quantitative insights for biomedical and molecular biology research, but often relies on computationally intensive datafitting methods to extract meaningful metrics. To address this l...

MAMSI: Integration of Multiassay Liquid Chromatography-Mass Spectrometry Metabolomics Data Using Multiview Machine Learning.

Analytical chemistry
Liquid chromatography-mass spectrometry (LC-MS) is a commonly used analytical technique in untargeted metabolomics. However, the diverse chemical and physical properties of metabolites often require the use of several different analytical assays for ...

Deep ensemble learning with transformer models for enhanced Alzheimer's disease detection.

Scientific reports
The progression of Alzheimer's disease is relentless, leading to a worsening of mental faculties over time. Currently, there is no remedy for this illness. Accurate detection and prompt intervention are pivotal in mitigating the progression of the di...

Enhanced particle swarm optimization for feature selection in SVM-based Alzheimer's disease diagnosis.

Scientific reports
Alzheimer's Disease (AD) is a progressive neurodegenerative disorder marked by neuronal loss, leading to cognitive and behavioral decline. With the aging global population, AD incidence and its socioeconomic burden are increasing. Developing effectiv...