精准心血管医学：人工智能和表观遗传学用于新生儿主动脉缩窄的发病机制和预测

数据集获取（Figshare）

• 源站标题：Precision cardiovascular medicine: artificial intelligence and epigenetics for the pathogenesis and prediction of coarctation in neonates
• DOI：10.6084/m9.figshare.11805795.v1
• 发布日期：2020-02-05
• 文件数：2 个
• 文件总体积：153.50 KB
• 源站页面：https://tandf.figshare.com/articles/dataset/Precision_cardiovascular_medicine_artificial_intelligence_and_epigenetics_for_the_pathogenesis_and_prediction_of_coarctation_in_neonates/11805795

命令行下载

curl -L -o "ijmf_a_1722995_sm3189.docx" "https://ndownloader.figshare.com/files/21552924"
curl -L -o "ijmf_a_1722995_sm3188.docx" "https://ndownloader.figshare.com/files/21552927"

文件清单

• ijmf_a_1722995_sm3189.docx（34.04 KB）
• ijmf_a_1722995_sm3188.docx（119.46 KB）

文件总体积 153.50 KB（源站 API 实测）。

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。

数据说明

Advances in omics and computational Artificial Intelligence (AI) have been said to be key to meeting the objectives of precision cardiovascular medicine. The focus of precision medicine includes a better assessment of disease risk and understanding of disease mechanisms. Our objective was to determine whether significant epigenetic changes occur in isolated, non-syndromic CoA. Further, we evaluated the AI analysis of DNA methylation for the prediction of CoA.
Genome-wide DNA methylation analysis of newborn blood DNA was performed in 24 isolated, non-syndromic CoA cases and 16 controls using the Illumina HumanMethylation450 BeadChip arrays. Cytosine nucleotide (CpG) methylation changes in CoA in each of 450,000 CpG loci were determined. Ingenuity pathway analysis (IPA) was performed to identify molecular and disease pathways that were epigenetically dysregulated. Using methylation data, six artificial intelligence (AI) platforms including deep learning (DL) was used for CoA detection.
We identified significant (FDR p-value ≤ .05) methylation changes in 65 different CpG sites located in 75 genes in CoA subjects. DL achieved an AUC (95% CI) = 0.97 (0.80–1) with 95% sensitivity and 98% specificity. Gene ontology (GO) analysis yielded epigenetic alterations in important cardiovascular developmental genes and biological processes: abnormal morphology of cardiovascular system, left ventricular dysfunction, heart conduction disorder, thrombus formation, and coronary artery disease.
In an exploratory study we report the use of AI and epigenomics to achieve important objectives of precision cardiovascular medicine. Accurate prediction of CoA was achieved using a newborn blood spot. Further, we provided evidence of a significant epigenetic etiology in isolated CoA development.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.6084/m9.figshare.11805795.v1（source: https://tandf.figshare.com/articles/dataset/Precision_cardiovascular_medicine_artificial_intelligence_and_epigenetics_for_the_pathogenesis_and_prediction_of_coarctation_in_neonates/11805795）。