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Bias-invariant RNA-sequencing metadata annotation.

Hannes WartmannSven HeinsKarin KloiberStefan Bonn
Published in: GigaScience (2022)
Using our novel domain adaptation approach, we achieved metadata annotation accuracies up to 15.7% better than a previously published method. Using the best model, we provide a list of >10,000 novel tissue and sex label annotations for 8,495 unique SRA samples. Our approach has the potential to revive idle datasets by automated annotation making them more searchable.
Keyphrases
  • rna seq
  • single cell
  • high throughput
  • machine learning
  • deep learning
  • randomized controlled trial
  • climate change