基于分位数回归森林的药物反应预测方法及可靠性评估数据集

数据集获取（Figshare）

• 源站标题：A quantile regression forest based method to predict drug response and assess prediction reliability
• DOI：10.1371/journal.pone.0205155
• 发布日期：2018-10-05
• 文件数：6 个
• 文件总体积：3.20 MB
• 源站页面：https://plos.figshare.com/articles/dataset/A_quantile_regression_forest_based_method_to_predict_drug_response_and_assess_prediction_reliability/7174586

命令行下载

curl -L -o "pone.0205155.s001.xlsx" "https://ndownloader.figshare.com/files/13196720"
curl -L -o "pone.0205155.s002.xlsx" "https://ndownloader.figshare.com/files/13196723"
curl -L -o "pone.0205155.s003.xlsx" "https://ndownloader.figshare.com/files/13196726"
curl -L -o "pone.0205155.s004.xlsx" "https://ndownloader.figshare.com/files/13196729"
curl -L -o "pone.0205155.s005.XLSX" "https://ndownloader.figshare.com/files/13196732"
curl -L -o "pone.0205155.s006.TXT" "https://ndownloader.figshare.com/files/13196735"

文件清单

• pone.0205155.s001.xlsx（11.07 KB）
• pone.0205155.s002.xlsx（18.58 KB）
• pone.0205155.s003.xlsx（1.21 MB）
• pone.0205155.s004.xlsx（118.04 KB）
• pone.0205155.s005.XLSX（1.51 MB）
• pone.0205155.s006.TXT（326.55 KB）

文件总体积 3.20 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

Drug response prediction is a critical step for personalized treatment of cancer patients and ultimately leads to precision medicine. A lot of machine-learning based methods have been proposed to predict drug response from different types of genomic data. However, currently available methods could only give a “point” prediction of drug response value but fail to provide the reliability and distribution of the prediction, which are of equal interest in clinical practice. In this paper, we proposed a method based on quantile regression forest and applied it to the CCLE dataset. Through the out-of-bag validation, our method achieved much higher prediction accuracy of drug response than other available tools. The assessment of prediction reliability by prediction intervals and its significance in personalized medicine were illustrated by several examples. Functional analysis of selected drug response associated genes showed that the proposed method achieves more biologically plausible results.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.1371/journal.pone.0205155（source: https://plos.figshare.com/articles/dataset/A_quantile_regression_forest_based_method_to_predict_drug_response_and_assess_prediction_reliability/7174586）。