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

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 developments. Reference: Hornung, R. & Wright, M. N. (2019) Block Forests: random forests for blocks of clinical and omics covariate data. BMC Bioinformatics 20:358. .


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install.packages("blockForest")

0.2.4 by Marvin N. Wright, 9 days ago


https://github.com/bips-hb/blockForest


Report a bug at https://github.com/bips-hb/blockForest/issues


Browse source code at https://github.com/cran/blockForest


Authors: Roman Hornung , Marvin N. Wright


Documentation:   PDF Manual  


GPL-3 license


Imports Rcpp, Matrix, methods, survival

Suggests testthat

Linking to Rcpp, RcppEigen


See at CRAN