Decision tree-based method for integrating gene expression, demographic, and clinical data to determine disease endotypes.

ABSTRACT:

Background

Complex diseases are often difficult to diagnose, treat and study due to the multi-factorial nature of the underlying etiology. Large data sets are now widely available that can be used to define novel, mechanistically distinct disease subtypes (endotypes) in a completely data-driven manner. However, significant challenges exist with regard to how to segregate individuals into suitable subtypes of the disease and understand the distinct biological mechanisms of each when the goal is to maximize the discovery potential of these data sets.

Results

A multi-step decision tree-based method is described for defining endotypes based on gene expression, clinical covariates, and disease indicators using childhood asthma as a case study. We attempted to use alternative appr

SUBMITTER: Williams-DeVane CR 

PROVIDER: S-EPMC4228284 | biostudies-literature | 2013 Nov

REPOSITORIES: biostudies-literature