排名前十的相关预测药物化合物

数据集获取（Figshare）

• 源站标题：The top ten related predicted drug compounds.
• DOI：10.1371/journal.pone.0333070.t002
• 发布日期：2025-10-06
• 文件数：1 个
• 文件总体积：5.63 KB
• 源站页面：https://plos.figshare.com/articles/dataset/The_top_ten_related_predicted_drug_compounds_/30288326

命令行下载

curl -L -o "Table 2.xls" "https://ndownloader.figshare.com/files/58521287"

文件清单

• Table 2.xls（5.63 KB）

文件总体积 5.63 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

Background
Primary Sjögren’s syndrome (pSS) is an autoimmune and inflammatory disorder that may affect the lungs, leading to interstitial lung disease (ILD). However, the diagnosis of progression from pSS to ILD is frequently delayed due to unstandardized interdisciplinary diagnostic criteria and a lack of reliable shared biomarkers. This diagnostic challenge, compounded by significant pathophysiological divergence in target organs, has hindered elucidation of their comorbidity mechanisms. This study employs integrated bioinformatics to identify shared biomarkers in pSS and ILD, deciphers their pathogenic mechanisms, and predicts targeted therapeutics via network pharmacology
Methods
From the Gene Expression Omnibus (GEO) database, we retrieved gene expression profiles of pSS and ILD. Differential expression gene (DEG) analysis was performed on the profiles, followed by further screening using four machine learning algorithms. Concurrently, weighted gene co-expression network analysis (WGCNA) was applied to identify gene modules, and enrichment analysis of WGCNA-derived genes was conducted to explore their biological functions. Genes obtained from WGCNA and machine learning approaches were then intersected to identify candidate biomarkers for pSS-ILD. The diagnostic potential of these candidate genes was evaluated in both discovery and validation sets using receiver operating characteristic (ROC) curves. Finally, we performed immune cell infiltration analysis of candidate genes, regulatory network construction for transcription factor (TF)-gene and miRNA-gene interactions, drug-target prediction, and molecular docking coupled with molecular dynamics simulations for predicted drugs.
Results
Differential expression analysis identified 25 shared genes between pSS and ILD gene expression profiles, with machine learning algorithms refining six key genes from these DEGs. WGCNA revealed 39 intersecting genes significantly enriched in biological processes including cell division, oocyte maturation, and metabolic regulation. Intersection of machine learning and WGCNA results yielded two hub genes (CYSLTR1 and SIGLEC10), both demonstrating robust diagnostic value in disco

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.1371/journal.pone.0333070.t002（source: https://plos.figshare.com/articles/dataset/The_top_ten_related_predicted_drug_compounds_/30288326）。