基于XGBoost和质谱的HBV相关肝病进展及肝细胞癌治疗代谢生物标志物识别数据集

数据集获取（Figshare）

• 源站标题：XGBoost- and
  Mass Spectrometry-Based Feature Selection
  for Identifying Metabolic Biomarkers Associated with HBV-Related Liver
  Disease Progression and Hepatocellular Carcinoma Treatment
• DOI：10.1021/acs.jproteome.5c00540.s001
• 发布日期：2025-10-15
• 文件数：1 个
• 文件总体积：60.42 KB
• 源站页面：https://acs.figshare.com/articles/dataset/XGBoost-_and_Mass_Spectrometry-Based_Feature_Selection_for_Identifying_Metabolic_Biomarkers_Associated_with_HBV-Related_Liver_Disease_Progression_and_Hepatocellular_Carcinoma_Treatment/30363518

命令行下载

curl -L -o "pr5c00540_si_001.xls" "https://ndownloader.figshare.com/files/58752146"

文件清单

• pr5c00540_si_001.xls（60.42 KB）

文件总体积 60.42 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

XGBoost, a gradient boosting algorithm, is widely recognized
for
its efficiency and robustness in multiclass classification tasks.
Metabolomics serves as a powerful tool for biomarker discovery; however,
metabolic biomarkers associated with the progression from chronic
hepatitis B (CHB) to liver cirrhosis (LC) to hepatocellular carcinoma
(HCC), as well as those related to treatment effects in HCC (HCCAT),
remain unclear. In this study, an XGBoost-based machine learning approach
combined with mass spectrometry was used to analyze the metabolic
profiles of 30 healthy controls (HC), 29 CHB patients, 30 LC patients,
30 HCC patients, and 30 HCCAT patients. Biomarker screening was conducted
through three comparative analyses: (1) HC, CHB, LC, HCC, and HCCAT;
(2) HC, CHB, LC, and HCC; and (3) HC, HCC, and HCCAT. A total of 17
metabolic biomarkers were identified, among which nine had not been
previously associated with HBV-related liver diseases. Notably, a
potential biomarker panel composed of eicosenoic acid, dihydromorphine,
cysteine, acetic acid, sitosterol, and hypoxanthine showed promise
for disease prognosis and therapeutic evaluation. These findings highlight
the great potential of integrating metabolomics with machine learning
to identify novel metabolic biomarkers related to HBV-associated liver
disease progression and treatment response.

许可

CC BY-NC 4.0

引用

学术使用请引用 DOI 10.1021/acs.jproteome.5c00540.s001（source: https://acs.figshare.com/articles/dataset/XGBoost-_and_Mass_Spectrometry-Based_Feature_Selection_for_Identifying_Metabolic_Biomarkers_Associated_with_HBV-Related_Liver_Disease_Progression_and_Hepatocellular_Carcinoma_Treatment/30363518）。