Multiplatform biomarker identification using a data-driven approach enables single-sample classification.

ABSTRACT:

Background

High-throughput gene expression profiles have allowed discovery of potential biomarkers enabling early diagnosis, prognosis and developing individualized treatment. However, it remains a challenge to identify a set of reliable and reproducible biomarkers across various gene expression platforms and laboratories for single sample diagnosis and prognosis. We address this need with our Data-Driven Reference (DDR) approach, which employs stably expressed housekeeping genes as references to eliminate platform-specific biases and non-biological variabilities.

Results

Our method identifies biomarkers with "built-in" features, and these features can be interpreted consistently regardless of profiling technology, which enable classification of single-sample independent of

SUBMITTER: Zhang L 

PROVIDER: S-EPMC6868758 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature