AIMC Topic: Apolipoproteins E

Clear Filters Showing 11 to 17 of 17 articles

Analysis of TEM micrographs with deep learning reveals APOE genotype-specific associations between HDL particle diameter and Alzheimer's dementia.

Cell reports methods Jan 27, 2025
High-density lipoprotein (HDL) particle diameter distribution is informative in the diagnosis of many conditions, including Alzheimer's disease (AD). However, obtaining an accurate HDL size measurement is challenging. We demonstrated the utility of m...
Humans Female Aged Particle Size Alzheimer Disease Aged, 80 and over Lipoproteins, HDL Male Apolipoproteins E Deep Learning Genotype
View on PubMed DOI

Classifying Alzheimer's Disease Neuropathology Using Clinical and MRI Measurements.

Journal of Alzheimer's disease : JAD Jan 1, 2024
BACKGROUND: Computer-aided machine learning models are being actively developed with clinically available biomarkers to diagnose Alzheimer's disease (AD) in living persons. Despite considerable work with cross-sectional in vivo data, many models lack...
Humans Aged Aged, 80 and over Male Middle Aged Machine Learning Brain Female Neuropathology Magnetic Resonance Imaging Alzheimer Disease Apolipoproteins E
View on PubMed DOI

Epistatic Features and Machine Learning Improve Alzheimer's Disease Risk Prediction Over Polygenic Risk Scores.

Journal of Alzheimer's disease : JAD Jan 1, 2024
BACKGROUND: Polygenic risk scores (PRS) are linear combinations of genetic markers weighted by effect size that are commonly used to predict disease risk. For complex heritable diseases such as late-onset Alzheimer's disease (LOAD), PRS models fail t...
Aged Male Genome-Wide Association Study Genetic Risk Score Apolipoproteins E Genetic Predisposition to Disease Epistasis, Genetic Humans Polymorphism, Single Nucleotide Multifactorial Inheritance Machine Learning Models, Genetic Female Alzheimer Disease
View on PubMed DOI

MRI-based Deep Learning Assessment of Amyloid, Tau, and Neurodegeneration Biomarker Status across the Alzheimer Disease Spectrum.

Radiology Oct 1, 2023
Background PET can be used for amyloid-tau-neurodegeneration (ATN) classification in Alzheimer disease, but incurs considerable cost and exposure to ionizing radiation. MRI currently has limited use in characterizing ATN status. Deep learning techniq...
Amyloid tau Proteins Apolipoproteins E Amyloid beta-Peptides Alzheimer Disease Positron-Emission Tomography Retrospective Studies Female Aged Male Deep Learning Cognitive Dysfunction Magnetic Resonance Imaging Humans Biomarkers
View on PubMed DOI

Machine Learning for the Prediction of Amyloid Positivity in Amnestic Mild Cognitive Impairment.

Journal of Alzheimer's disease : JAD Jan 1, 2021
BACKGROUND: Amyloid-β (Aβ) evaluation in amnestic mild cognitive impairment (aMCI) patients is important for predicting conversion to Alzheimer's disease. However, Aβ evaluation through Aβ positron emission tomography (PET) is limited due to high cos...
Humans Machine Learning Algorithms Female Cognitive Dysfunction Cohort Studies Magnetic Resonance Imaging Predictive Value of Tests Area Under Curve Aged, 80 and over Amyloid beta-Peptides Male Positron-Emission Tomography Middle Aged Disease Progression Apolipoproteins E Reproducibility of Results Mental Recall Hippocampus Risk Factors Cerebral Cortex Aged Neuropsychological Tests
View on PubMed DOI

Optimizing Machine Learning Methods to Improve Predictive Models of Alzheimer's Disease.

Journal of Alzheimer's disease : JAD Jan 1, 2019
BACKGROUND: Predicting clinical course of cognitive decline can boost clinical trials' power and improve our clinical decision-making. Machine learning (ML) algorithms are specifically designed for the purpose of prediction; however. identifying opti...
Humans Cerebral Cortex Machine Learning Apolipoproteins E Prognosis Demography Male Middle Aged Female Cognitive Dysfunction Longitudinal Studies Predictive Value of Tests Aged Support Vector Machine Magnetic Resonance Imaging Discriminant Analysis Aged, 80 and over Models, Neurological Alzheimer Disease Hippocampus
View on PubMed DOI

Development of a Late-Life Dementia Prediction Index with Supervised Machine Learning in the Population-Based CAIDE Study.

Journal of Alzheimer's disease : JAD Jan 1, 2017
BACKGROUND AND OBJECTIVE: This study aimed to develop a late-life dementia prediction model using a novel validated supervised machine learning method, the Disease State Index (DSI), in the Finnish population-based CAIDE study.
Severity of Illness Index Community Health Planning Dementia ROC Curve Neuropsychological Tests Cognition Reproducibility of Results Finland Humans Predictive Value of Tests Risk Factors Apolipoproteins E Supervised Machine Learning Retrospective Studies Aged Cerebrovascular Disorders Female Male
View on PubMed DOI
Popular Topics
Recent Journals