Joint deep learning for batch effect removal and classification toward MALDI MS based metabolomics.

ABSTRACT:

Background

Metabolomics is a primary omics topic, which occupies an important position in both clinical applications and basic researches for metabolic signatures and biomarkers. Unfortunately, the relevant studies are challenged by the batch effect caused by many external factors. In last decade, the technique of deep learning has become a dominant tool in data science, such that one may train a diagnosis network from a known batch and then generalize it to a new batch. However, the batch effect inevitably hinders such efforts, as the two batches under consideration can be highly mismatched.

Results

We propose an end-to-end deep learning framework, for joint batch effect removal and then classification upon metabolomics data. We firstly validate the proposed deep learning f

SUBMITTER: Niu J 

PROVIDER: S-EPMC9275160 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature