Block Forests: random forests for blocks of clinical and omics covariate data.

ABSTRACT:

Background

In the last years more and more multi-omics data are becoming available, that is, data featuring measurements of several types of omics data for each patient. Using multi-omics data as covariate data in outcome prediction is both promising and challenging due to the complex structure of such data. Random forest is a prediction method known for its ability to render complex dependency patterns between the outcome and the covariates. Against this background we developed five candidate random forest variants tailored to multi-omics covariate data. These variants modify the split point selection of random forest to incorporate the block structure of multi-omics data and can be applied to any outcome type for which a random forest variant exists, such as categorical, continuo

SUBMITTER: Hornung R 

PROVIDER: S-EPMC6598279 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature