TabDEG: Classifying differentially expressed genes from RNA-seq data based on feature extraction and deep learning framework.

ABSTRACT: Traditional differential expression genes (DEGs) identification models have limitations in small sample size datasets because they require meeting distribution assumptions, otherwise resulting high false positive/negative rates due to sample variation. In contrast, tabular data model based on deep learning (DL) frameworks do not need to consider the data distribution types and sample variation. However, applying DL to RNA-Seq data is still a challenge due to the lack of proper labeling and the small sample size compared to the number of genes. Data augmentation (DA) extracts data features using different methods and procedures, which can significantly increase complementary pseudo-values from limited data without significant additional cost. Based on this, we combine DA and DL framework-ba

SUBMITTER: Feng S 

PROVIDER: S-EPMC11262683 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature