blockForest: Random Forests for Blocks of Clinical and Omics Covariate Data

Roman Hornung, Marvin N. Wright

Description

A random forest variant suitable for the prediction of binary, survival and continuous outcomes using multi-omics data, i.e., data for which measurements of different types of omics data and/or clinical data for each patient exist. Examples include gene expression measurements, methylation measurements, and copy number variation measurements.

References

• Hornung, R. & Wright, M. N. (2019). Block Forests: random forests for blocks of clinical and omics covariate data. BMC Bioinformatics 20:358. https://doi.org/10.1186/s12859-019-2942-y.
• Wright, M. N. & Ziegler, A. (2017). ranger: A fast implementation of random forests for high dimensional data in C++ and R. J Stat Softw 77:1-17. https://doi.org/10.18637/jss.v077.i01.
• Breiman, L. (2001). Random forests. Mach Learn, 45:5-32. https://doi.org/10.1023/A:1010933404324.