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

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 struc...

Full description

Bibliographic Details
Main Authors: Roman Hornung, Marvin N. Wright
Format: Article
Language:English
Published: BMC 2019-06-01
Series:BMC Bioinformatics
Subjects:
Multi-omics data
Prediction
Random forest
Machine learning
Statistics
Survival analysis
Online Access:http://link.springer.com/article/10.1186/s12859-019-2942-y

Internet

http://link.springer.com/article/10.1186/s12859-019-2942-y

Similar Items