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bioRxiv · 10.1101/2023.04.25.538300

SeqImprove: Machine Learning Assisted Creation of Machine Readable Sequence Information

Mante, J.·Sents, Z.·Myers, C. J.
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Abstract

The 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.

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Mante, J., Sents, Z., Myers, C. J.. 2023-04-28. SeqImprove: Machine Learning Assisted Creation of Machine Readable Sequence Information. https://doi.org/10.1101/2023.04.25.538300

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