基于核贝叶斯矩阵分解的整合与个性化癌症QSAR分析

数据集获取（Figshare）

• 源站标题：Integrative
  and Personalized QSAR Analysis in Cancer
  by Kernelized Bayesian Matrix Factorization
• DOI：10.1021/ci500152b.s002
• 发布日期：2014-08-25
• 文件数：1 个
• 文件总体积：4.64 MB
• 源站页面：https://acs.figshare.com/articles/dataset/Integrative_and_Personalized_QSAR_Analysis_in_Cancer_by_Kernelized_Bayesian_Matrix_Factorization/2261623

命令行下载

curl -L -o "ci500152b_si_002.zip" "https://ndownloader.figshare.com/files/3897619"

文件清单

• ci500152b_si_002.zip（4.64 MB）

文件总体积 4.64 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

With
data from recent large-scale drug sensitivity measurement
campaigns, it is now possible to build and test models predicting
responses for more than one hundred anticancer drugs against several
hundreds of human cancer cell lines. Traditional quantitative structure–activity
relationship (QSAR) approaches focus on small molecules in searching
for their structural properties predictive of the biological activity
in a single cell line or a single tissue type. We extend this line
of research in two directions: (1) an integrative QSAR approach predicting
the responses to new drugs for a panel of multiple known cancer cell
lines simultaneously and (2) a personalized QSAR approach predicting
the responses to new drugs for new cancer cell lines. To solve the
modeling task, we apply a novel kernelized Bayesian matrix factorization
method. For maximum applicability and predictive performance, the
method optionally utilizes genomic features of cell lines and target
information on drugs in addition to chemical drug descriptors. In
a case study with 116 anticancer drugs and 650 cell lines, we demonstrate
the usefulness of the method in several relevant prediction scenarios,
differing in the amount of available information, and analyze the
importance of various types of drug features for the response prediction.
Furthermore, after predicting the missing values of the data set,
a complete global map of drug response is explored to assess treatment
potential and treatment range of therapeutically interesting anticancer
drugs.

许可

CC BY-NC 4.0

引用

学术使用请引用 DOI 10.1021/ci500152b.s002（source: https://acs.figshare.com/articles/dataset/Integrative_and_Personalized_QSAR_Analysis_in_Cancer_by_Kernelized_Bayesian_Matrix_Factorization/2261623）。