基于患者来源异种移植(PDX)的高维精准医学数据集
基于PDX研究的高维数据，用于估计最优个体化治疗规则并识别多癌种生物标志物。
基本信息
资源简介
基于患者来源异种移植(PDX)的高维精准医学数据集
【一句话概览】
本数据集是 figshare 公开的结构化表格数据集，收录 4 个文件、合计 60.16 MB，主题涵盖 Biomarkers、Deep learning autoencoders、Machine learning、Outcome weighted learning、Precision medicine，可用于统计建模、特征工程与队列分析，CC BY 4.0 许可开放获取。
【数据集背景】
本数据集源站标题为《High-Dimensional Precision Medicine From Patient-Derived Xenografts》。配套论文《High-Dimensional Precision Medicine From Patient-Derived Xenografts》发表于 Journal of the American Statistical Association（2020），DOI：10.1080/01621459.2020.1828091。源站首次发布：2020-11-12。
【数据内容】
源站原始描述：The complexity of human cancer often results in significant heterogeneity in response to treatment. Precision medicine offers the potential to improve patient outcomes by leveraging this heterogeneity. Individualized treatment rules (ITRs) formalize precision medicine as maps from the patient covariate space into the space of allowable treatments. The optimal ITR is that which maximizes the mean of a clinical outcome in a population of interest. Patient-derived xenograft (PDX) studies permit the…
【数据结构与技术规格】
文件数 4 个，合计 60.16 MB。PDX.Code_v3.zip：application/zip，1.21 MB。uasa_a_1828091_sm5856.docx：application/vnd.openxmlformats-officedocument.wordprocessingml.document，14.40 KB。数据集 DOI：10.6084/m9.figshare.13014165.v2。
【主题与分类】
源站学科分类：Medicine、Pharmacology、Biotechnology、Biological Sciences not elsewhere classified、Information Systems not elsewhere classified、Cancer。主题标签：Biomarkers、Deep learning autoencoders、Machine learning、Outcome weighted learning、Precision medicine、Q-learning。
【适用方向】
结构化表格数据可直接用于统计建模、特征工程与队列分析。
【获取与许可】
源站页面：https://tandf.figshare.com/articles/dataset/High_dimensional_precision_medicine_from_patient-derived_xenografts/13014165
使用许可：CC BY 4.0（https://creativecommons.org/licenses/by/4.0/）。
规范引用：High-Dimensional Precision Medicine From Patient-Derived Xenografts. Taylor & Francis. Rashid, Naim U.; Luckett, Daniel J.; Chen, Jingxiang; Lawson, Michael T.; Wang, Longshaokan; Zhang, Yunshu; et al
【使用注意】
· 源站最后修改于 2024-02-26，引用时请注意版本时效。
下载信息
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暂未开放当前数据集主要来源为 Kaggle 公开托管，完整影像包建议通过原始链接或 Kaggle API 获取。
使用方式
数据集获取（Figshare）
- 源站标题：High-Dimensional Precision Medicine From Patient-Derived Xenografts
- DOI：10.6084/m9.figshare.13014165.v2
- 发布日期：2020-11-12
- 文件数：4 个
- 文件总体积：60.16 MB
- 源站页面：https://tandf.figshare.com/articles/dataset/High_dimensional_precision_medicine_from_patient-derived_xenografts/13014165
命令行下载
curl -L -o "PDX.Code_v3.zip" "https://ndownloader.figshare.com/files/24815883"
curl -L -o "PDX.Data.zip" "https://ndownloader.figshare.com/files/24815886"
curl -L -o "uasa_a_1828091_sm5856.docx" "https://ndownloader.figshare.com/files/24815889"
curl -L -o "uasa_a_1828091_sm5851.pdf" "https://ndownloader.figshare.com/files/24815892"
文件清单
PDX.Code_v3.zip（1.21 MB）PDX.Data.zip（56.76 MB）uasa_a_1828091_sm5856.docx（14.40 KB）uasa_a_1828091_sm5851.pdf（2.17 MB）
文件总体积 60.16 MB（源站 API 实测）。
数据说明
The complexity of human cancer often results in significant heterogeneity in response to treatment. Precision medicine offers the potential to improve patient outcomes by leveraging this heterogeneity. Individualized treatment rules (ITRs) formalize precision medicine as maps from the patient covariate space into the space of allowable treatments. The optimal ITR is that which maximizes the mean of a clinical outcome in a population of interest. Patient-derived xenograft (PDX) studies permit the evaluation of multiple treatments within a single tumor, and thus are ideally suited for estimating optimal ITRs. PDX data are characterized by correlated outcomes, a high-dimensional feature space, and a large number of treatments. Here we explore machine learning methods for estimating optimal ITRs from PDX data. We analyze data from a large PDX study to identify biomarkers that are informative for developing personalized treatment recommendations in multiple cancers. We estimate optimal ITRs using regression-based (Q-learning) and direct-search methods (outcome weighted learning). Finally, we implement a superlearner approach to combine multiple estimated ITRs and show that the resulting ITR performs better than any of the input ITRs, mitigating uncertainty regarding user choice. Our results indicate that PDX data are a valuable resource for developing individualized treatment strategies in oncology. Supplementary materials for this article are available online.
许可
CC BY 4.0
引用
学术使用请引用 DOI 10.6084/m9.figshare.13014165.v2（source: https://tandf.figshare.com/articles/dataset/High_dimensional_precision_medicine_from_patient-derived_xenografts/13014165）。
数据缺失？
依托陕西、四川两大基地，我们与超过十家三甲医院建立直接合作关系，覆盖合规授权、采集治理、专业标注、数据交付的全流程，为AI医疗团队提供即拿即用的高质量临床数据。
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