阿尔茨海默病风险人群中认知韧性相关基因的发现

数据集获取

源站：https://synapse.org/Synapse:syn68174792

标准获取流程：

1. 访问源站链接，注册/登录账号；
2. 进入数据集详情页，按页面指引申请权限或直接下载；
3. 部分平台提供 SDK，例如：
   • OpenDataLab: pip install openxlab 后 openxlab.dataset.get(...)
   • ModelScope: pip install modelscope 后 modelscope download
   • 天池: 登录后页面提供直链下载
     以源站「数据下载/使用文档」页面为准。

数据说明

Manuscript: Discovery of Genes Underlying Cognitive Resilience in Individuals Predisposed to Alzheimer’s Risk Authors: Wei Tsai, Caitlin E. McNiff, Joseph S. Reddy, Xue Wang, Zachary S. Quicksall, Kwangsik Nho, Amy R. Dunn, Michael Heckman, Yingxue Ren, Na Zhao, Kejal Kantarci, Michelle M. Mielke, Ronald C. Petersen, Catherine C. Kaczorowski, Minerva M. Carrasquillo, Mariet Allen, Andrew J. Saykin, for the Alzheimer’s Disease Neuroimaging Initiative, Nilüfer Ertekin-Taner Preprint URL: NA Published URL: Not yet available. Dataset contact: Dr. Mariet Allen, Mayo Clinic @mxa24 Code Availability: syn68156578 — Data use acknowledgement: Please acknowledge the data contributor in publications based on secondary data use by including the following statement and references in your manuscript. >“The results published here are in whole or in part based on data obtained from the AD Knowledge Portal (https://adknowledgeportal.org/).” >MCSA: The generation of genetic and gene expression measures from Mayo Clinic Study of Aging (MCSA) was led by Dr. Nilüfer Ertekin-Taner at the Mayo Clinic, Jacksonville, FL as part of the multi-PI RF1AG051504 (MPIs Bu and Ertekin-Taner) and R01AG061796 (Ertekin-Taner). This data would not be possible without the participation of research volunteers and the contribution of data by collaborating researchers." Cite: which one should this be? the doi of my paper once it’s published? >Mayo RNAseq: "The Mayo RNAseq study data was led by Dr. Nilüfer Ertekin-Taner, Mayo Clinic, Jacksonville, FL as part of the multi-PI U01 AG046139 (MPIs Golde, Ertekin-Taner, Younkin, Price). Samples were provided from the following sources: The Mayo Clinic Brain Bank. Data collection was supported through funding by NIA grants P50 AG016574, R01 AG032990, U01 AG046139, R01 AG018023, U01 AG006576, U01 AG006786, R01 AG025711, R01 AG017216, R01 AG003949, NINDS grant R01 NS080820, Cu

数据加载示例（图像类）

from PIL import Image
import glob, os

files = (glob.glob(os.path.join(path, "**", "*.png"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.jpg"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.tif"), recursive=True))
print("图像文件数:", len(files))
img = Image.open(files[0]); print("尺寸/模式:", img.size, img.mode)

# torchvision Dataset 方式:
# from torchvision import datasets
# ds = datasets.ImageFolder(path)  # 要求 子目录=类别

目录组织与标注格式以源站说明和下载后实际文件为准。