Testing results for classifying AD/MCI, AD/NC and MCI/NC.
收藏NIAID Data Ecosystem2026-05-01 收录
数据链接：
官方服务：
资源简介：
Testing results for classifying AD/MCI, AD/NC and MCI/NC.
应用场景：
创建时间：
2024-04-19
相关数据集
Table8_Development and validation of immune-based biomarkers and deep learning models for Alzheimer’s disease.XLSX
Background: Alzheimer’s disease (AD) is the most common form of dementia in old age and poses a severe threat to the health and life of the elderly. However, traditional diagnostic methods and the ATN
NIAID Data Ecosystem100
Amyloid and cerebrovascular burden divergently influence brain functional network changes over time
Objective: To examine the effects of baseline Alzheimer’s disease and cerebrovascular disease markers on longitudinal default mode network (DMN) and executive control network (ECN) functional connecti
NIAID Data Ecosystem40
Table_1_A Novel Key Features Screening Method Based on Extreme Learning Machine for Alzheimer’s Disease Study.XLSX
The Extreme Learning Machine (ELM) is a simple and efficient Single Hidden Layer Feedforward Neural Network(SLFN) algorithm. In recent years, it has been gradually used in the study of Alzheimer’s dis
NIAID Data Ecosystem10
Additional file 1 of Amyloid-β misfolding as a plasma biomarker indicates risk for future clinical Alzheimer’s disease in individuals with subjective cognitive decline
Additional file 1: Supplementary Table 1. Cox proportional hazard regression models including other forms of dementia.
NIAID Data Ecosystem60
Table_1_Morphological, Structural, and Functional Networks Highlight the Role of the Cortical-Subcortical Circuit in Individuals With Subjective Cognitive Decline.docx
Subjective cognitive decline (SCD) is considered the earliest stage of the clinical manifestations of the continuous progression of Alzheimer’s Disease (AD). Previous studies have suggested that multi
NIAID Data Ecosystem30
