Abstract
Motivation: Machine learning (ML) methods are frequently used in Omics research to examine associations between molecular data and e.g., exposures and health conditions. ML is also used for
feature selection to facilitate biological interpretation. Our previous MUVR algorithm was shown to generate predictions and variable selections at state-of-the-art performance. However, a general framework for assessing modeling fitness is still lacking. In addition, enabling to adjust for covariates is a highly desired, but largely lacking trait in ML. We aimed to address these issues in the new MUVR2 framework.
Results: The MUVR2 algorithm was developed to include the regularized regression framework elastic net in addition to partial least squares and random forest modeling. Compared with other cross-validation strategies, MUVR2 consistently showed state-of-the-art performance, including variable selection, while minimizing overfitting. Testing on simulated and real-world data, we also showed that MUVR2 allows for the adjustment for covariates using elastic net modeling, but not using partial least squares or random forest.
Availability: Algorithms, data, scripts and a tutorial are open source under GPL-3 license and available in the MUVR2 R package at
https://github.com/MetaboComp/MUVR2.
Supplementary information: Supplementary data are available at Bioinformatics Advances online.