基于最小基因组信息和化学性质的随机森林模型预测药物对癌细胞活性

数据集获取（Figshare）

• 源站标题：Predicting drug activity against cancer cells by random forest models based on minimal genomic information and chemical properties
• DOI：10.1371/journal.pone.0219774
• 发布日期：2019-07-11
• 文件数：4 个
• 文件总体积：1.46 MB
• 源站页面：https://plos.figshare.com/articles/dataset/Predicting_drug_activity_against_cancer_cells_by_random_forest_models_based_on_minimal_genomic_information_and_chemical_properties/8861909

命令行下载

curl -L -o "pone.0219774.s001.tif" "https://ndownloader.figshare.com/files/16243067"
curl -L -o "pone.0219774.s002.tif" "https://ndownloader.figshare.com/files/16243070"
curl -L -o "pone.0219774.s003.docx" "https://ndownloader.figshare.com/files/16243073"
curl -L -o "pone.0219774.s004.docx" "https://ndownloader.figshare.com/files/16243076"

文件清单

• pone.0219774.s001.tif（984.65 KB）
• pone.0219774.s002.tif（301.36 KB）
• pone.0219774.s003.docx（74.83 KB）
• pone.0219774.s004.docx（103.51 KB）

文件总体积 1.46 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

A key goal of precision medicine is predicting the best drug therapy for a specific patient from genomic information. In oncology, cancers that appear similar pathologically can vary greatly in how they respond to the same drug. Fortunately, data from high-throughput screening programs often reveal important relationships between genomic variability of cancer cells and their response to drugs. Nevertheless, many current computational methods to predict compound activity against cancer cells require large quantities of genomic, epigenomic, and additional cellular data to develop and to apply. Here we integrate recent screening data and machine learning to train classification models that predict the activity/inactivity of compounds against cancer cells based on the mutational status of only 145 oncogenes and a set of compound structural descriptors. Using IC50 values of 1 μM as activity cutoffs, our predictive models have sensitivities of 87%, specificities of 87%, and yield an area under the receiver operating characteristic curve equal to 0.94. We also develop regression models to predict log(IC50) values of compounds for cancer cells; the models achieve a Pearson correlation coefficient of 0.86 for cross-validation and up to 0.65–0.73 against blind test sets. Predictive performance remains strong when as few as 50 oncogenes are included. Finally, even when 40% of experimental IC50 values are missing from screening data, they can be imputed with sufficient reliability that classification accuracy is not diminished. The presented models are fast to generate and may serve as easily implemented screening tools for personalized oncology medicine, drug repurposing, and drug discovery.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.1371/journal.pone.0219774（source: https://plos.figshare.com/articles/dataset/Predicting_drug_activity_against_cancer_cells_by_random_forest_models_based_on_minimal_genomic_information_and_chemical_properties/8861909）。