Promor：一个用于无标记蛋白质组学数据分析和预测建模的综合R软件包。
promor: a comprehensive R package for label-free proteomics data analysis and predictive modeling.
作者信息
Ranathunge Chathurani, Patel Sagar S, Pinky Lubna, Correll Vanessa L, Chen Shimin, Semmes O John, Armstrong Robert K, Combs C Donald, Nyalwidhe Julius O
机构信息
Eastern Virginia Medical School, School of Health Professions, Norfolk, VA 23501, USA.
The Leroy T. Canoles Jr. Cancer Research Center, Eastern Virginia Medical School, Norfolk, VA 23501, USA.
出版信息
Bioinform Adv. 2023 Mar 7;3(1):vbad025. doi: 10.1093/bioadv/vbad025. eCollection 2023.
SUMMARY
We present promor, a comprehensive, user-friendly R package that streamlines label-free quantification proteomics data analysis and building machine learning-based predictive models with top protein candidates.
AVAILABILITY AND IMPLEMENTATION
promor is freely available as an open source R package on the Comprehensive R Archive Network (CRAN) (https://CRAN.R-project.org/package=promor) and distributed under the Lesser General Public License (version 2.1 or later). Development version of promor is maintained on GitHub (https://github.com/caranathunge/promor) and additional documentation and tutorials are provided on the package website (https://caranathunge.github.io/promor/).
SUPPLEMENTARY INFORMATION
Supplementary data are available at online.
摘要
我们展示了promor，这是一个全面且用户友好的R包，它简化了无标记定量蛋白质组学数据分析，并利用顶级蛋白质候选物构建基于机器学习的预测模型。
可用性与实现
promor作为一个开源R包在综合R存档网络（CRAN）（https://CRAN.R-project.org/package=promor）上免费提供，并根据GNU较宽松通用公共许可证（版本2.1或更高版本）分发。promor的开发版本在GitHub（https://github.com/caranathunge/promor）上维护，并且在包网站（https://caranathunge.github.io/promor/）上提供了额外的文档和教程。
补充信息
补充数据可在网上获取。
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