癌细胞系剂量反应

癌细胞系对通路抑制剂的剂量反应数据,包含基于CRISPR基因依赖性的差异分析和突变-药物反应比较。

Figshare
2023-12-20 更新
浏览 24
表格xlink "> investigatingmediated gene knockoutidentifying risk factorsfree survival ratesdrug response profilesdevising effective therapiescrucial vulnerabilities associatedcell fitness postanalysis also revealedpharmacological dependencies acrosscombat genetic disorderscancer genome atlas
Dose-response of cancer cell lines.

基本信息

模态
表格
大小
0.04488911 GB
许可
CC BY 4.0
创建/更新时间
2023-12-20
版本
v1

资源简介

癌细胞系剂量反应

【数据集背景】

本数据集源站标题为《Dose-response of cancer cell lines.》。源站首次发布:2023-12-20。

【数据内容】

源站原始描述:The spreadsheet contains the following results/datasets according to the sheet name. Between Cell line Dose Responses: mean difference comparison of the dose-responses to pathway inhibitors between the cancer cell lines that have a higher dependence on dark and light genes and those with a lower dependence on dark and light genes as defined using the CRISPR-derived gene dependence scores (see methods section). Mutations Gene Drug Response: mean dose-response comparison between cell lines that ha…

【数据结构与技术规格】

文件数 1 个,合计 44.89 MB。数据集 DOI:10.1371/journal.pone.0296029.s013。

【主题与分类】

源站学科分类:Medicine、Microbiology、Genetics、Molecular Biology、Pharmacology、Biological Sciences not elsewhere classified。主题标签:xlink "> investigating、mediated gene knockout、identifying risk factors、free survival rates、drug response profiles、devising effective therapies、crucial vulnerabilities associated、cell fitness post、analysis also revealed、pharmacological dependencies across。

【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://plos.figshare.com/articles/dataset/Dose-response_of_cancer_cell_lines_/24880071

使用许可:CC BY 4.0(https://creativecommons.org/licenses/by/4.0/)。

规范引用:Kafita, Doris; Nkhoma, Panji; Dzobo, Kevin; Sinkala, Musalula (2023). Dose-response of cancer cell lines.. PLOS ONE. Dataset

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下载信息

注册下载

需要注册 Kaggle 账号并登录后下载,适合需要跟踪下载记录和使用 API 的用户。

暂未开放

公开下载

无需注册即可直接获取公开样本或文档,适合快速预览和评估数据集质量。

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公开数据集受托下载与技术交付服务。

提供高速下载与技术交付服务(收技术服务费,非数据销售)

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)

命令行下载

curl -L -o "pone.0296029.s013.xlsx" "https://ndownloader.figshare.com/files/43776084"

文件清单

  • pone.0296029.s013.xlsx(44.89 MB)

文件总体积 44.89 MB(源站 API 实测)。

数据说明

The spreadsheet contains the following results/datasets according to the sheet name. Between Cell line Dose Responses: mean difference comparison of the dose-responses to pathway inhibitors between the cancer cell lines that have a higher dependence on dark and light genes and those with a lower dependence on dark and light genes as defined using the CRISPR-derived gene dependence scores (see methods section). Mutations Gene Drug Response: mean dose-response comparison between cell lines that have a mutation(s) in a particular gene versus those that do not have a mutation(s) in that particular gene. Pathways: List of pathways from which our analyses are based. Pathway Inhibitors: List of pathway inhibitors from which our analyses are based. The rest of the sheets contain drug sensitive and resistant genes for all 24 pathways (e.g., PI3K MTOR), PI3K MTOR CRISPR: genes that are associated with significantly increased sensitivity to different pathway inhibitors in cell lines demonstrating higher dependence on the specific gene(s) for their fitness. And genes that are associated with a significant resistance to different pathway inhibitors in cell lines demonstrating higher dependence on the specific gene(s) for their fitness. PI3K MTOR Mutation: genes that are associated with significantly increased sensitivity to different pathway inhibitors in cell lines that have mutations in the specific gene. And genes that are associated with significant resistance to different pathway inhibitors in cell lines that have mutations in the specific gene.
(XLSX)

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.1371/journal.pone.0296029.s013(source: https://plos.figshare.com/articles/dataset/Dose-response_of_cancer_cell_lines_/24880071)。

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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