An interpretable deep learning framework for genome-informed precision oncology
用于基因组指导的精准肿瘤学可解释深度学习框架的预处理数据，包含SGA、细胞状态和药物反应预测。
基本信息
资源简介
An interpretable deep learning framework for genome-informed precision oncology
【数据集背景】
源站首次发布：2024-05-31。
【数据内容】
源站原始描述：Cancers result from aberrations in cellular signaling systems, typically resulting from driver somatic genome alterations (SGAs) in individual tumors. Precision oncology requires understanding the cellular state and selecting medications that induce vulnerability in cancer cells under such conditions. To this end, we developed a computational framework consisting of two components: 1) A representation-learning component, which learns a representation of the cellular signaling systems when pertur…
【数据结构与技术规格】
文件数 1 个，合计 357.49 MB。数据集 DOI：10.6084/m9.figshare.25943893.v2。
【主题与分类】
源站学科分类：Genomics and transcriptomics、Biological network analysis、Bioinformatic methods development。主题标签：ResGitDRPrecision Oncology, Drug sensitivity prediction。
【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://figshare.com/articles/dataset/Processed_data_for_this_project/25943893
使用许可：CC BY 4.0（https://creativecommons.org/licenses/by/4.0/）。
规范引用：Ren, Shuangxia; F. Cooper, Gregory; Chen, Lujia; Lu, Xinghua (2024). An interpretable deep learning framework for genome-informed precision oncology. figshare. Dataset
下载信息
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使用方式
数据集获取（Figshare）
- 源站标题：<b>An interpretable deep learning framework for genome-informed precision oncology</b>
- DOI：10.6084/m9.figshare.25943893.v2
- 发布日期：2024-05-31
- 文件数：1 个
- 文件总体积：357.49 MB
- 源站页面：https://figshare.com/articles/dataset/Processed_data_for_this_project/25943893
命令行下载
curl -L -o "data.zip" "https://ndownloader.figshare.com/files/46748740"
文件清单
data.zip（357.49 MB）
文件总体积 357.49 MB（源站 API 实测）。
数据说明
Cancers result from aberrations in cellular signaling systems, typically resulting from driver somatic genome alterations (SGAs) in individual tumors. Precision oncology requires understanding the cellular state and selecting medications that induce vulnerability in cancer cells under such conditions. To this end, we developed a computational framework consisting of two components: 1) A representation-learning component, which learns a representation of the cellular signaling systems when perturbed by SGAs, using a biologically motivated and interpretable deep learning model. 2) A drug-response-prediction component, which predicts drug response by leveraging the information of the cellular state of the cancer cells derived by the first component. Our cell-state-oriented framework significantly improves drug response prediction accuracy compared to models using SGAs directly in cell lines. Moreover, our model performs well with real patient data. Importantly, our framework enables the prediction of response to chemotherapy agents based on SGAs, thus expanding genome-informed precision oncology beyond molecularly targeted drugs.
许可
CC BY 4.0
引用
学术使用请引用 DOI 10.6084/m9.figshare.25943893.v2（source: https://figshare.com/articles/dataset/Processed_data_for_this_project/25943893）。
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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