Bias-invariant RNA-sequencing metadata annotation.

ABSTRACT:

Background

Recent technological advances have resulted in an unprecedented increase in publicly available biomedical data, yet the reuse of the data is often precluded by experimental bias and a lack of annotation depth and consistency. Missing annotations makes it impossible for researchers to find datasets specific to their needs.

Findings

Here, we investigate RNA-sequencing metadata prediction based on gene expression values. We present a deep-learning-based domain adaptation algorithm for the automatic annotation of RNA-sequencing metadata. We show, in multiple experiments, that our model is better at integrating heterogeneous training data compared with existing linear regression-based approaches, resulting in improved tissue type classification. By using a model archit

SUBMITTER: Wartmann H 

PROVIDER: S-EPMC8559615 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature