非甾体抗炎药在信号通路和miRNA调控通路中的效应建模

数据集获取（Figshare）

• 源站标题：Modeling of Non-Steroidal Anti-Inflammatory Drug Effect within Signaling Pathways and miRNA-Regulation Pathways
• DOI：10.1371/journal.pone.0072477
• 发布日期：2013-08-14
• 文件数：4 个
• 文件总体积：1.08 MB
• 源站页面：https://plos.figshare.com/articles/dataset/_Modeling_of_Non_Steroidal_Anti_Inflammatory_Drug_Effect_within_Signaling_Pathways_and_miRNA_Regulation_Pathways_/772670

命令行下载

curl -L -o "Information_S1.doc" "https://ndownloader.figshare.com/files/1169935"
curl -L -o "Information_S2.xls" "https://ndownloader.figshare.com/files/1169940"
curl -L -o "Information_S3.txt" "https://ndownloader.figshare.com/files/1169941"
curl -L -o "Information_S4.csv" "https://ndownloader.figshare.com/files/1169942"

文件清单

• Information_S1.doc（1.00 MB）
• Information_S2.xls（30.21 KB）
• Information_S3.txt（3.90 KB）
• Information_S4.csv（46.18 KB）

文件总体积 1.08 MB（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

To date, it is widely recognized that Non-Steroidal Anti-Inflammatory Drugs (NSAIDs) can exert considerable anti-tumor effects regarding many types of cancers. The prolonged use of NSAIDs is highly associated with diverse side effects. Therefore, tailoring down the NSAID application onto individual patients has become a necessary and relevant step towards personalized medicine. This study conducts the systemsbiological approach to construct a molecular model (NSAID model) containing a cyclooxygenase (COX)-pathway and its related signaling pathways. Four cancer hallmarks are integrated into the model to reflect different developmental aspects of tumorigenesis. In addition, a Flux-Comparative-Analysis (FCA) based on Petri net is developed to transfer the dynamic properties (including drug responsiveness) of individual cellular system into the model. The gene expression profiles of different tumor-types with available drug-response information are applied to validate the predictive ability of the NSAID model. Moreover, two therapeutic developmental strategies, synthetic lethality and microRNA (miRNA) biomarker discovery, are investigated based on the COX-pathway. In conclusion, the result of this study demonstrates that the NSAID model involving gene expression, gene regulation, signal transduction, protein interaction and other cellular processes, is able to predict the individual cellular responses for different therapeutic interventions (such as NS-398 and COX-2 specific siRNA inhibition). This strongly indicates that this type of model is able to reflect the physiological, developmental and pathological processes of an individual. The approach of miRNA biomarker discovery is demonstrated for identifying miRNAs with oncogenic and tumor suppressive functions for individual cell lines of breast-, colon- and lung-tumor. The achieved results are in line with different independent studies that investigated miRNA biomarker related to diagnostics of cancer treatments, therefore it might shed light on the development of biomarker discovery at individual level. Particular results of this study might contribute to step further towards personalized medicine with the system

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.1371/journal.pone.0072477（source: https://plos.figshare.com/articles/dataset/_Modeling_of_Non_Steroidal_Anti_Inflammatory_Drug_Effect_within_Signaling_Pathways_and_miRNA_Regulation_Pathways_/772670）。