Analyzing biomarker discovery: Estimating the reproducibility of biomarker sets.

ABSTRACT: Many researchers try to understand a biological condition by identifying biomarkers. This is typically done using univariate hypothesis testing over a labeled dataset, declaring a feature to be a biomarker if there is a significant statistical difference between its values for the subjects with different outcomes. However, such sets of proposed biomarkers are often not reproducible - subsequent studies often fail to identify the same sets. Indeed, there is often only a very small overlap between the biomarkers proposed in pairs of related studies that explore the same phenotypes over the same distribution of subjects. This paper first defines the Reproducibility Score for a labeled dataset as a measure (taking values between 0 and 1) of the reproducibility of the results produced by a spec

SUBMITTER: Forouzandeh A 

PROVIDER: S-EPMC9333302 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature