Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, 4362 Esch-sur-Alzette, Luxembourg.
Bioinformatics. 2021 Nov 5;37(21):3889-3895. doi: 10.1093/bioinformatics/btab576.
Multivariate (multi-target) regression has the potential to outperform univariate (single-target) regression at predicting correlated outcomes, which frequently occur in biomedical and clinical research. Here we implement multivariate lasso and ridge regression using stacked generalization.
Our flexible approach leads to predictive and interpretable models in high-dimensional settings, with a single estimate for each input-output effect. In the simulation, we compare the predictive performance of several state-of-the-art methods for multivariate regression. In the application, we use clinical and genomic data to predict multiple motor and non-motor symptoms in Parkinson's disease patients. We conclude that stacked multivariate regression, with our adaptations, is a competitive method for predicting correlated outcomes.
The R package joinet is available on GitHub (https://github.com/rauschenberger/joinet) and cran (https://cran.r-project.org/package=joinet).
Supplementary data are available at Bioinformatics online.
多变量（多目标）回归有可能比单变量（单目标）回归在预测相关结果方面表现更优，而相关结果在生物医学和临床研究中经常出现。在这里，我们使用堆叠泛化来实现多元lasso 和 ridge 回归。
我们灵活的方法在高维环境中生成了具有预测能力和可解释性的模型，每个输入-输出效果只有一个单一的估计值。在模拟中，我们比较了多种用于多元回归的最先进方法的预测性能。在应用中，我们使用临床和基因组数据来预测帕金森病患者的多种运动和非运动症状。我们的结论是，经过我们的改编，堆叠多元回归是一种具有竞争力的预测相关结果的方法。
R 包 joinet 可在 GitHub（https://github.com/rauschenberger/joinet）和 cran（https://cran.r-project.org/package=joinet）上获得。
补充数据可在生物信息学在线获得。
