AIMC Topic: Alzheimer Disease
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Large-scale plasma proteomic profiling unveils diagnostic biomarkers and pathways for Alzheimer's disease.
Nature aging
Jun 1, 2025
Proteomic studies have been instrumental in identifying brain, cerebrospinal fluid and plasma proteins associated with Alzheimer's disease (AD). Here, we comprehensively examined 6,905 aptamers corresponding to 6,106 unique proteins in plasma in more...
ML-Driven Alzheimer's disease prediction: A deep ensemble modeling approach.
SLAS technology
Jun 1, 2025
Alzheimer's disease (AD) is a progressive neurological disorder characterized by cognitive decline due to brain cell death, typically manifesting later in life.Early and accurate detection is critical for effective disease management and treatment. T...
Machine learning prediction prior to onset of mild cognitive impairment using T1-weighted magnetic resonance imaging radiomic of the hippocampus.
Asian journal of psychiatry
Jun 1, 2025
BACKGROUND: Early identification of individuals who progress from normal cognition (NC) to mild cognitive impairment (MCI) may help prevent cognitive decline. We aimed to build predictive models using radiomic features of the bilateral hippocampus in...
Deep-Diffeomorphic Networks for Conditional Brain Templates.
Human brain mapping
Jun 1, 2025
Deformable brain templates are an important tool in many neuroimaging analyses. Conditional templates (e.g., age-specific templates) have advantages over single population templates by enabling improved registration accuracy and capturing common proc...
Exploring the diagnostic potential of EEG theta power and interhemispheric correlation of temporal lobe activities in Alzheimer's Disease through random forest analysis.
Computers in biology and medicine
Jun 1, 2025
BACKGROUND: Considering the prevalence of Alzheimer's Disease (AD) among the aging population and the limited means of treatment, early detection emerges as a crucial focus area whereas electroencephalography (EEG) provides a promising diagnostic too...
Identification and prioritization of disease candidate genes using biomedical named entity recognition and random forest classification.
Computers in biology and medicine
Jun 1, 2025
BACKGROUND AND OBJECTIVE: The elucidation of candidate genes is fundamental to comprehending intricate diseases, vital for early diagnosis, personalized treatment, and drug discovery. Traditional Disease Gene Identification methods encounter limitati...
Multimodal fusion architectures for Alzheimer's disease diagnosis: An experimental study.
Journal of biomedical informatics
Jun 1, 2025
OBJECTIVE: In the attempt of early diagnosis of Alzheimer's Disease, varying forms of medical records of multiple modalities are gathered to seize the interaction of multiple factors. However, the heterogeneity of multimodal data brings a challenge. ...
Deep learning-based triple-tracer brain PET scanning in a single session: A simulation study using clinical data.
NeuroImage
Jun 1, 2025
OBJECTIVES: Multiplexed Positron Emission Tomography (PET) imaging allows simultaneous acquisition of multiple radiotracer signals, thus enhancing diagnostic capabilities, reducing scan times, and improving patient comfort. Traditional methods often ...
GeneDX-PBMC: An adversarial autoencoder framework for unlocking Alzheimer's disease biomarkers using blood single-cell RNA sequencing data.
Computers in biology and medicine
Jun 1, 2025
OBJECTIVE: To identify blood-based biomarkers and therapeutic targets for Alzheimer's disease (AD) by leveraging single-cell RNA sequencing (scRNA-seq) data from peripheral blood mononuclear cells (PBMCs) and advanced deep learning techniques.
Deep-learning based multi-modal models for brain age, cognition and amyloid pathology prediction.
Alzheimer's research & therapy
May 31, 2025
BACKGROUND: Magnetic resonance imaging (MRI), combined with artificial intelligence techniques, has improved our understanding of brain structural change and enabled the estimation of brain age. Neurodegenerative disorders, such as Alzheimer's diseas...
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