SeqImprove: Machine Learning Assisted Creation of Machine Readable Sequence Information Journal Article uri icon



  • AbstractThe progress and utility of synthetic biology is currently hindered by the lengthy process of studying literature and replicating poorly documented work. Reconstruction of crucial design information through post-hoc curation is highly noisy and error-prone. To combat this, author participation during the curation process is crucial. To encour-age author participation without overburdening them, an ML-assisted curation tool called SeqImprove has been developed. Using named entity recognition, named entity normalization, and sequence matching, SeqImprove creates machine-readable sequence data and metadata annotations, which authors can then review and edit before sub-mitting a final sequence file. SeqImprove makes it easier for authors to submit FAIR sequence data that is findable, accessible, interoperable, and reusable.

publication date

  • April 28, 2023

has restriction

  • green

Date in CU Experts

  • May 3, 2023 9:59 AM

Full Author List

  • Mante J; Sents Z; Myers CJ

author count

  • 3

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