Data Sheet 1_使用机器学习方法识别线粒体相关特征基因预测2型糖尿病

数据集获取（Figshare）

• 源站标题：Data Sheet 1_Identification of mitochondria-related feature genes for predicting type 2 diabetes mellitus using machine learning methods.csv
• DOI：10.3389/fendo.2025.1501159.s001
• 发布日期：2025-03-27
• 文件数：1 个
• 文件总体积：57.57 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Identification_of_mitochondria-related_feature_genes_for_predicting_type_2_diabetes_mellitus_using_machine_learning_methods_csv/28676399

命令行下载

curl -L -o "Data Sheet 1_Identification of mitochondria-related feature genes for predicting type 2 diabetes mellitus using machine learning methods.csv" "https://ndownloader.figshare.com/files/53263652"

文件清单

• Data Sheet 1_Identification of mitochondria-related feature genes for predicting type 2 diabetes mellitus using machine learning methods.csv（57.57 KB）

文件总体积 57.57 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

PurposeWe aimed to identify the mitochondria-related feature genes associated with type 2 diabetes mellitus and explore their potential roles in immune cell infiltration.
MethodsDatasets from GSE41762, GSE38642, GSE25724, and GSE20966 were obtained from the Gene Expression Omnibus database. Weighted Gene Co-expression Network Analysis was performed to achieve mitochondria-related hub genes. Random Forest, Least Absolute Shrinkage and Selection Operator, and Support Vector Machines-Recursive Feature Elimination algorithms were used to screen mitochondria-related feature genes. Receiver Operating Characteristic analysis was applied to evaluate the accuracy of the feature genes. Pearson’s correlation analysis was used to calculate the correlations between feature genes and immune cell infiltration. The prediction of candidate drugs targeting the feature genes were predicted using the DGIdb database. qRT-PCR was performed to access the mRNA expressions of the feature genes.
ResultsFive mitochondria-related feature genes (SLC2A2, ENTPD3, ARG2, CHL1, and RASGRP1) were identified for type 2 diabetes mellitus prediction. They possessed high predictive accuracies with the area under the Receiver Operating Characteristic curve values >0.8. All five genes showed the strongest positive correlation with regulatory T cells and negative correlation with neutrophils. Additionally, drugs prediction analysis revealed 2(S)-amino-6-boronohexanoic acid, difluoromethylornithine, and compound 9 could target ARG2, while metformin was a candidate drug for SCL2A2. Finally, all five genes were confirmed to be decreased in MIN6 cells treated with high glucose and palmitic acid.
ConclusionSLC2A2, ENTPD3, ARG2, CHL1, and RASGRP1 could be used as the mitochondria-related feature genes to predict type 2 diabetes mellitus and the therapeutic targets.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fendo.2025.1501159.s001（source: https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Identification_of_mitochondria-related_feature_genes_for_predicting_type_2_diabetes_mellitus_using_machine_learning_methods_csv/28676399）。