数据表1：基于Java的深度神经网络药物反应预测方法的开发与评估

数据集获取（Figshare）

• 源站标题：Data_Sheet_1_Development and evaluation of a java-based deep neural network method for drug response predictions.PDF
• DOI：10.3389/frai.2023.1069353.s001
• 发布日期：2023-03-23
• 文件数：1 个
• 文件总体积：615.40 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Development_and_evaluation_of_a_java-based_deep_neural_network_method_for_drug_response_predictions_PDF/22322932

命令行下载

curl -L -o "Data_Sheet_1_Development and evaluation of a java-based deep neural network method for drug response predictions.PDF" "https://ndownloader.figshare.com/files/39712117"

文件清单

• Data_Sheet_1_Development and evaluation of a java-based deep neural network method for drug response predictions.PDF（615.40 KB）

文件总体积 615.40 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

Accurate prediction of drug response is a crucial step in personalized medicine. Recently, deep learning techniques have been witnessed with significant breakthroughs in a variety of areas including biomedical research and chemogenomic applications. This motivated us to develop a novel deep learning platform to accurately and reliably predict the response of cancer cells to different drug treatments. In the present work, we describe a Java-based implementation of deep neural network method, termed JavaDL, to predict cancer responses to drugs solely based on their chemical features. To this end, we devised a novel cost function and added a regularization term which suppresses overfitting. We also adopted an early stopping strategy to further reduce overfit and improve the accuracy and robustness of our models. To evaluate our method, we compared with several popular machine learning and deep neural network programs and observed that JavaDL either outperformed those methods in model building or obtained comparable predictions. Finally, JavaDL was employed to predict drug responses of several aggressive breast cancer cell lines, and the results showed robust and accurate predictions with r2 as high as 0.81.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/frai.2023.1069353.s001（source: https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Development_and_evaluation_of_a_java-based_deep_neural_network_method_for_drug_response_predictions_PDF/22322932）。