DSPLMF：一种使用逻辑矩阵分解中新型正则化方法预测癌症药物敏感性的数据集

数据集获取（Figshare）

• 源站标题：DataSheet_1_DSPLMF: A Method for Cancer Drug Sensitivity Prediction Using a Novel Regularization Approach in Logistic Matrix Factorization.csv
• DOI：10.3389/fgene.2020.00075.s001
• 发布日期：2020-02-27
• 文件数：1 个
• 文件总体积：50.85 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/DataSheet_1_DSPLMF_A_Method_for_Cancer_Drug_Sensitivity_Prediction_Using_a_Novel_Regularization_Approach_in_Logistic_Matrix_Factorization_csv/11906340

命令行下载

curl -L -o "DataSheet_1_DSPLMF: A Method for Cancer Drug Sensitivity Prediction Using a Novel Regularization Approach in Logistic Matrix Factorization.csv" "https://ndownloader.figshare.com/files/21834993"

文件清单

• DataSheet_1_DSPLMF: A Method for Cancer Drug Sensitivity Prediction Using a Novel Regularization Approach in Logistic Matrix Factorization.csv（50.85 KB）

文件总体积 50.85 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

The ability to predict the drug response for cancer disease based on genomics information is an essential problem in modern oncology, leading to personalized treatment. By predicting accurate anticancer responses, oncologists achieve a complete understanding of the effective treatment for each patient. In this paper, we present DSPLMF (Drug Sensitivity Prediction using Logistic Matrix Factorization) approach based on Recommender Systems. DSPLMF focuses on discovering effective features of cell lines and drugs for computing the probability of the cell lines are sensitive to drugs by logistic matrix factorization approach. Since similar cell lines and similar drugs may have similar drug responses and incorporating similarities between cell lines and drugs can potentially improve the drug response prediction, gene expression profile, copy number alteration, and single-nucleotide mutation information are used for cell line similarity and chemical structures of drugs are used for drug similarity. Evaluation of the proposed method on CCLE and GDSC datasets and comparison with some of the state-of-the-art methods indicates that the result of DSPLMF is significantly more accurate and more efficient than these methods. To demonstrate the ability of the proposed method, the obtained latent vectors are used to identify subtypes of cancer of the cell line and the predicted IC50 values are used to depict drug-pathway associations. The source code of DSPLMF method is available in https://github.com/emdadi/DSPLMF.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fgene.2020.00075.s001（source: https://frontiersin.figshare.com/articles/dataset/DataSheet_1_DSPLMF_A_Method_for_Cancer_Drug_Sensitivity_Prediction_Using_a_Novel_Regularization_Approach_in_Logistic_Matrix_Factorization_csv/11906340）。