AIMC Topic: Alzheimer Disease

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Heterogeneous Graph Contrastive Learning with Graph Diffusion for Drug Repositioning.

Journal of chemical information and modeling
Drug repositioning, which identifies novel therapeutic applications for existing drugs, offers a cost-effective alternative to traditional drug development. However, effectively capturing the complex relationships between drugs and diseases remains c...

Deep learning reveals pathology-confirmed neuroimaging signatures in Alzheimer's, vascular and Lewy body dementias.

Brain : a journal of neurology
Concurrent neurodegenerative and vascular pathologies pose a diagnostic challenge in the clinical setting, with histopathology remaining the definitive modality for dementia-type diagnosis. To address this clinical challenge, we introduce a neuropath...

Radiomics of PET Using Neural Networks for Prediction of Alzheimer's Disease Diagnosis.

Statistics in medicine
Positron emission tomography (PET) imaging technology is widely used for diagnosing Alzheimer's disease (AD) in people with dementia. Although various computational methods have been proposed for diagnosis of AD using PET images, prediction of diseas...

Prediction Model and Nomogram for Amyloid Positivity Using Clinical and MRI Features in Individuals With Subjective Cognitive Decline.

Human brain mapping
There is an urgent need for the precise prediction of cerebral amyloidosis using noninvasive and accessible indicators to facilitate the early diagnosis of individuals with the preclinical stage of Alzheimer's disease (AD). Two hundred and four indiv...

Large-scale plasma proteomic profiling unveils diagnostic biomarkers and pathways for Alzheimer's disease.

Nature aging
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
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
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
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
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
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...