通过基因表达谱鉴定炎症性肠病的新型预测分类器

数据集获取（Figshare）

• 源站标题：Identification of Novel Predictor Classifiers for Inflammatory Bowel Disease by Gene Expression Profiling
• DOI：10.1371/journal.pone.0076235
• 发布日期：2013-10-14
• 文件数：5 个
• 文件总体积：1.45 MB
• 源站页面：https://plos.figshare.com/articles/dataset/_Identification_of_Novel_Predictor_Classifiers_for_Inflammatory_Bowel_Disease_by_Gene_Expression_Profiling_/821522

命令行下载

curl -L -o "Figure_S1.tif" "https://ndownloader.figshare.com/files/1234912"
curl -L -o "Table_S1.doc" "https://ndownloader.figshare.com/files/1234913"
curl -L -o "Table_S2.doc" "https://ndownloader.figshare.com/files/1234914"
curl -L -o "Table_S3.doc" "https://ndownloader.figshare.com/files/1234915"
curl -L -o "Table_S4.docx" "https://ndownloader.figshare.com/files/1234916"

文件清单

• Figure_S1.tif（1.15 MB）
• Table_S1.doc（81.92 KB）
• Table_S2.doc（61.44 KB）
• Table_S3.doc（79.87 KB）
• Table_S4.docx（75.73 KB）

文件总体积 1.45 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

Background
Improvement of patient quality of life is the ultimate goal of biomedical research, particularly when dealing with complex, chronic and debilitating conditions such as inflammatory bowel disease (IBD). This is largely dependent on receiving an accurate and rapid diagnose, an effective treatment and in the prediction and prevention of side effects and complications. The low sensitivity and specificity of current markers burden their general use in the clinical practice. New biomarkers with accurate predictive ability are needed to achieve a personalized approach that take the inter-individual differences into consideration.
Methods
We performed a high throughput approach using microarray gene expression profiling of colon pinch biopsies from IBD patients to identify predictive transcriptional signatures associated with intestinal inflammation, differential diagnosis (Crohn’s disease or ulcerative colitis), response to glucocorticoids (resistance and dependence) or prognosis (need for surgery). Class prediction was performed with self-validating Prophet software package.
Results
Transcriptional profiling divided patients in two subgroups that associated with degree of inflammation. Class predictors were identified with predictive accuracy ranging from 67 to 100%. The expression accuracy was confirmed by real time-PCR quantification. Functional analysis of the predictor genes showed that they play a role in immune responses to bacteria (PTN, OLFM4 and LILRA2), autophagy and endocytocis processes (ATG16L1, DNAJC6, VPS26B, RABGEF1, ITSN1 and TMEM127) and glucocorticoid receptor degradation (STS and MMD2).
Conclusions
We conclude that using analytical algorithms for class prediction discovery can be useful to uncover gene expression profiles and identify classifier genes with potential stratification utility of IBD patients, a major step towards personalized therapy.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.1371/journal.pone.0076235（source: https://plos.figshare.com/articles/dataset/_Identification_of_Novel_Predictor_Classifiers_for_Inflammatory_Bowel_Disease_by_Gene_Expression_Profiling_/821522）。