GEOlimma: differential expression analysis and feature selection using pre-existing microarray data.

ABSTRACT:

Background

Differential expression and feature selection analyses are essential steps for the development of accurate diagnostic/prognostic classifiers of complicated human diseases using transcriptomics data. These steps are particularly challenging due to the curse of dimensionality and the presence of technical and biological noise. A promising strategy for overcoming these challenges is the incorporation of pre-existing transcriptomics data in the identification of differentially expressed (DE) genes. This approach has the potential to improve the quality of selected genes, increase classification performance, and enhance biological interpretability. While a number of methods have been developed that use pre-existing data for differential expression analysis, existing methods d

SUBMITTER: Lu L 

PROVIDER: S-EPMC7860207 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature