A random forest based biomarker discovery and power analysis framework for diagnostics research.

ABSTRACT:

Background

Biomarker identification is one of the major and important goal of functional genomics and translational medicine studies. Large scale -omics data are increasingly being accumulated and can provide vital means for the identification of biomarkers for the early diagnosis of complex disease and/or for advanced patient/diseases stratification. These tasks are clearly interlinked, and it is essential that an unbiased and stable methodology is applied in order to address them. Although, recently, many, primarily machine learning based, biomarker identification approaches have been developed, the exploration of potential associations between biomarker identification and the design of future experiments remains a challenge.

Methods

In this study, using both simulated and

SUBMITTER: Acharjee A 

PROVIDER: S-EPMC7685541 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature