利用单一疗法数据和 IDACombo 预测联合用药疗效的网络应用程序。

Yunong Xia, Alexander L Ling, Weijie Zhang, Adam Lee, Mei-Chi Su, Robert F Gruener, Sampreeti Jena, Yingbo Huang, Siddhika Pareek, Yuting Shan, R Stephanie Huang
{"title":"利用单一疗法数据和 IDACombo 预测联合用药疗效的网络应用程序。","authors":"Yunong Xia, Alexander L Ling, Weijie Zhang, Adam Lee, Mei-Chi Su, Robert F Gruener, Sampreeti Jena, Yingbo Huang, Siddhika Pareek, Yuting Shan, R Stephanie Huang","doi":"10.26502/jcsct.5079218","DOIUrl":null,"url":null,"abstract":"<p><p>We recently reported a computational method (IDACombo) designed to predict the efficacy of cancer drug combinations using monotherapy response data and the assumptions of independent drug action. Given the strong agreement between IDACombo predictions and measured drug combination efficacy in vitro and in clinical trials, we believe IDACombo can be of immediate use to researchers who are working to develop novel drug combinations. While we previously released our method as an R package, we have now created an R Shiny application to allow researchers without programming experience to easily utilize this method. The app provides a graphical interface which enables users to easily generate efficacy predictions with IDACombo using provided data from several high-throughput cell line screens or using custom, user-provided data.</p>","PeriodicalId":73634,"journal":{"name":"Journal of cancer science and clinical therapeutics","volume":"7 4","pages":"253-258"},"PeriodicalIF":0.0000,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10852200/pdf/","citationCount":"0","resultStr":"{\"title\":\"A Web Application for Predicting Drug Combination Efficacy Using Monotherapy Data and IDACombo.\",\"authors\":\"Yunong Xia, Alexander L Ling, Weijie Zhang, Adam Lee, Mei-Chi Su, Robert F Gruener, Sampreeti Jena, Yingbo Huang, Siddhika Pareek, Yuting Shan, R Stephanie Huang\",\"doi\":\"10.26502/jcsct.5079218\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>We recently reported a computational method (IDACombo) designed to predict the efficacy of cancer drug combinations using monotherapy response data and the assumptions of independent drug action. Given the strong agreement between IDACombo predictions and measured drug combination efficacy in vitro and in clinical trials, we believe IDACombo can be of immediate use to researchers who are working to develop novel drug combinations. While we previously released our method as an R package, we have now created an R Shiny application to allow researchers without programming experience to easily utilize this method. The app provides a graphical interface which enables users to easily generate efficacy predictions with IDACombo using provided data from several high-throughput cell line screens or using custom, user-provided data.</p>\",\"PeriodicalId\":73634,\"journal\":{\"name\":\"Journal of cancer science and clinical therapeutics\",\"volume\":\"7 4\",\"pages\":\"253-258\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10852200/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of cancer science and clinical therapeutics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.26502/jcsct.5079218\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2023/12/8 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of cancer science and clinical therapeutics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.26502/jcsct.5079218","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2023/12/8 0:00:00","PubModel":"Epub","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

我们最近报告了一种计算方法(IDACombo),旨在利用单药治疗反应数据和独立药物作用假设预测抗癌药物组合的疗效。鉴于 IDACombo 预测结果与体外和临床试验中测得的联合用药疗效非常吻合,我们相信 IDACombo 可以立即为致力于开发新型联合用药的研究人员所用。我们以前曾以 R 软件包的形式发布过我们的方法,现在我们创建了一个 R Shiny 应用程序,让没有编程经验的研究人员也能轻松使用这种方法。该应用程序提供了一个图形界面,用户可以使用从多个高通量细胞系筛选中提供的数据或用户提供的自定义数据,通过 IDACombo 轻松生成药效预测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
A Web Application for Predicting Drug Combination Efficacy Using Monotherapy Data and IDACombo.

We recently reported a computational method (IDACombo) designed to predict the efficacy of cancer drug combinations using monotherapy response data and the assumptions of independent drug action. Given the strong agreement between IDACombo predictions and measured drug combination efficacy in vitro and in clinical trials, we believe IDACombo can be of immediate use to researchers who are working to develop novel drug combinations. While we previously released our method as an R package, we have now created an R Shiny application to allow researchers without programming experience to easily utilize this method. The app provides a graphical interface which enables users to easily generate efficacy predictions with IDACombo using provided data from several high-throughput cell line screens or using custom, user-provided data.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
A Web Application for Predicting Drug Combination Efficacy Using Monotherapy Data and IDACombo. Biophysical and Biological Mechanisms of Tumor Treating Fields in Glioblastoma. Small-Cell Lung Cancer in a Cancer Center in Colombia Epidemiology, Treatment, and Evolution of Glioblastoma in a Low-Income Country: Moroccan Experience Mycobacterium Bovis-Related Abdominal Aortic Aneurysm after Intravesical Bacillus Calmette-Guérin Therapy for Non-Muscle Invasive Bladder Cancer
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1