A random forest variant 'block forest' ('BlockForest') tailored to the
prediction of binary, survival and continuous outcomes using block-structured
covariate data, for example, clinical covariates plus measurements of a certain
omics data type or multi-omics data, that is, data for which measurements of
different types of omics data and/or clinical data for each patient exist. Examples
of different omics data types include gene expression measurements, mutation data
and copy number variation measurements.
Block forest are presented in Hornung & Wright (2019). The package includes four
other random forest variants for multi-omics data: 'RandomBlock', 'BlockVarSel',
'VarProb', and 'SplitWeights'. These were also considered in Hornung & Wright (2019),
but performed worse than block forest in their comparison study based on 20 real
multi-omics data sets. Therefore, we recommend to use block forest ('BlockForest')
in applications. The other random forest variants can, however, be consulted for
academic purposes, for example, in the context of further methodological
Reference: Hornung, R. & Wright, M. N. (2019) Block Forests: random forests for blocks of clinical and omics covariate data. BMC Bioinformatics 20:358.