Embedding covariate adjustments in tree-based automated machine learning for biomedical big data analyses

Embedding covariate adjustments in tree-based automated machine learning for biomedical big data analyses

Abstract Background A typical task in bioinformatics consists of identifying which features are associated with a target outcome of interest and building a predictive model. Automated machine learning (AutoML) systems such as the Tree-based Pipeline Optimization Tool (TPOT) constitute an appealing a...

Full description

Bibliographic Details
Main Authors: Elisabetta Manduchi, Weixuan Fu, Joseph D. Romano, Stefano Ruberto, Jason H. Moore
Format: Article
Language:English
Published: BMC 2020-10-01
Series:BMC Bioinformatics
Subjects:
AutoML
Covariate adjustment
Genetic programming
Pathways
Feature importance
Online Access:http://link.springer.com/article/10.1186/s12859-020-03755-4

Internet

http://link.springer.com/article/10.1186/s12859-020-03755-4

Similar Items