Joint Research Center for Computational Biomedicine (JRC-COMBINE), RWTH Aachen University, Aachen, Germany.
Aachen Institute for Advanced Study in Computational Engineering Science (AICES), RWTH Aachen University, Aachen, Germany.
Bioinformatics. 2019 Oct 1;35(19):3846-3848. doi: 10.1093/bioinformatics/btz145.
Translational models that utilize omics data generated in in vitro studies to predict the drug efficacy of anti-cancer compounds in patients are highly distinct, which complicates the benchmarking process for new computational approaches. In reaction to this, we introduce the uniFied translatiOnal dRug rESponsE prEdiction platform FORESEE, an open-source R-package. FORESEE not only provides a uniform data format for public cell line and patient datasets, but also establishes a standardized environment for drug response prediction pipelines, incorporating various state-of-the-art pre-processing methods, model training algorithms and validation techniques. The modular implementation of individual elements of the pipeline facilitates a straightforward development of combinatorial models, which can be used to re-evaluate and improve already existing pipelines as well as to develop new ones.
FORESEE is licensed under GNU General Public License v3.0 and available at https://github.com/JRC-COMBINE/FORESEE and https://doi.org/10.17605/OSF.IO/RF6QK, and provides vignettes for documentation and application both online and in the Supplementary Files 2 and 3.
Supplementary data are available at Bioinformatics online.
利用体外研究中生成的组学数据来预测抗癌化合物在患者中的药物疗效的转化模型差异很大,这使得新的计算方法的基准测试过程变得复杂。针对这一问题,我们引入了统一的转化药物反应预测平台 FORESEE,这是一个开源的 R 包。FORESEE 不仅为公共细胞系和患者数据集提供了统一的数据格式,还为药物反应预测管道建立了标准化的环境,整合了各种最先进的预处理方法、模型训练算法和验证技术。管道中各个元素的模块化实现促进了组合模型的直接开发,可用于重新评估和改进现有的管道,以及开发新的管道。
FORESEE 根据 GNU 通用公共许可证 v3.0 获得许可,可在 https://github.com/JRC-COMBINE/FORESEE 和 https://doi.org/10.17605/OSF.IO/RF6QK 上获得,并在在线和补充文件 2 和 3 中提供文档和应用程序的详细信息。
补充数据可在生物信息学在线获得。