数据表1：机器学习结合因果关系推断揭示COVID-19与急性心肌梗死共享病理机制

数据集获取（Figshare）

• 源站标题：Data_Sheet_1_Machine learning approach combined with causal relationship inferring unlocks the shared pathomechanism between COVID-19 and acute myocardial infarction.XLSX
• DOI：10.3389/fmicb.2023.1153106.s001
• 发布日期：2023-03-29
• 文件数：1 个
• 文件总体积：68.31 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Machine_learning_approach_combined_with_causal_relationship_inferring_unlocks_the_shared_pathomechanism_between_COVID-19_and_acute_myocardial_infarction_XLSX/22352251

命令行下载

curl -L -o "Data_Sheet_1_Machine learning approach combined with causal relationship inferring unlocks the shared pathomechanism between COVID-19 and acute myocar.XLSX" "https://ndownloader.figshare.com/files/39769798"

文件清单

• Data_Sheet_1_Machine learning approach combined with causal relationship inferring unlocks the shared pathomechanism between COVID-19 and acute myocar.XLSX（68.31 KB）

文件总体积 68.31 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

BackgroundIncreasing evidence suggests that people with Coronavirus Disease 2019 (COVID-19) have a much higher prevalence of Acute Myocardial Infarction (AMI) than the general population. However, the underlying mechanism is not yet comprehended. Therefore, our study aims to explore the potential secret behind this complication.
Materials and methodsThe gene expression profiles of COVID-19 and AMI were acquired from the Gene Expression Omnibus (GEO) database. After identifying the differentially expressed genes (DEGs) shared by COVID-19 and AMI, we conducted a series of bioinformatics analytics to enhance our understanding of this issue.
ResultsOverall, 61 common DEGs were filtered out, based on which we established a powerful diagnostic predictor through 20 mainstream machine-learning algorithms, by utilizing which we could estimate if there is any risk in a specific COVID-19 patient to develop AMI. Moreover, we explored their shared implications of immunology. Most remarkably, through the Bayesian network, we inferred the causal relationships of the essential biological processes through which the underlying mechanism of co-pathogenesis between COVID-19 and AMI was identified.
ConclusionFor the first time, the approach of causal relationship inferring was applied to analyzing shared pathomechanism between two relevant diseases, COVID-19 and AMI. Our findings showcase a novel mechanistic insight into COVID-19 and AMI, which may benefit future preventive, personalized, and precision medicine.
Graphical abstract

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fmicb.2023.1153106.s001（source: https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Machine_learning_approach_combined_with_causal_relationship_inferring_unlocks_the_shared_pathomechanism_between_COVID-19_and_acute_myocardial_infarction_XLSX/22352251）。