DataSheet1_特征选择将药物反应预测从细胞系转化为患者

数据集获取（Figshare）

• 源站标题：DataSheet1_Feature selection translates drug response predictors from cell lines to patients.pdf
• DOI：10.3389/fgene.2023.1217414.s001
• 发布日期：2023-07-14
• 文件数：1 个
• 文件总体积：2.75 MB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/DataSheet1_Feature_selection_translates_drug_response_predictors_from_cell_lines_to_patients_pdf/23683221

命令行下载

curl -L -o "DataSheet1_Feature selection translates drug response predictors from cell lines to patients.pdf" "https://ndownloader.figshare.com/files/41560419"

文件清单

• DataSheet1_Feature selection translates drug response predictors from cell lines to patients.pdf（2.75 MB）

文件总体积 2.75 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

Targeted therapies and chemotherapies are prevalent in cancer treatment. Identification of predictive markers to stratify cancer patients who will respond to these therapies remains challenging because patient drug response data are limited. As large amounts of drug response data have been generated by cell lines, methods to efficiently translate cell-line-trained predictors to human tumors will be useful in clinical practice. Here, we propose versatile feature selection procedures that can be combined with any classifier. For demonstration, we combined the feature selection procedures with a (linear) logit model and a (non-linear) K-nearest neighbor and trained these on cell lines to result in LogitDA and KNNDA, respectively. We show that LogitDA/KNNDA significantly outperforms existing methods, e.g., a logistic model and a deep learning method trained by thousands of genes, in prediction AUC (0.70–1.00 for seven of the ten drugs tested) and is interpretable. This may be due to the fact that sample sizes are often limited in the area of drug response prediction. We further derive a novel adjustment on the prediction cutoff for LogitDA to yield a prediction accuracy of 0.70–0.93 for seven drugs, including erlotinib and cetuximab, whose pathways relevant to anti-cancer therapies are also uncovered. These results indicate that our methods can efficiently translate cell-line-trained predictors into tumors.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fgene.2023.1217414.s001（source: https://frontiersin.figshare.com/articles/dataset/DataSheet1_Feature_selection_translates_drug_response_predictors_from_cell_lines_to_patients_pdf/23683221）。