A novel meta-analysis based on data augmentation and elastic data shared lasso regularization for gene expression.

ABSTRACT:

Background

Gene expression analysis can provide useful information for analyzing complex biological mechanisms. However, many reported findings are unrepeatable due to small sample sizes relative to a large number of genes and the low signal-to-noise ratios of most gene expression datasets.

Results

Meta-analysis of multi-data sets is an efficient method for tackling the above problem. To improve the performance of meta-analysis, we propose a novel meta-analysis framework. It consists of two parts: (1) a novel data augmentation strategy. Various cross-platform normalization methods exist, which can preserve original biological information of gene expression datasets from different angles and add different "perturbations" to the dataset. Using such perturbation, we provide a f

SUBMITTER: Huang HH 

PROVIDER: S-EPMC9396780 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature