SeqImprove: Machine Learning Assisted Creation of Machine Readable Sequence Information
Mante, J.; Sents, Z.; Myers, C. J.
Show 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.
Matching journals
The top 7 journals account for 50% of the predicted probability mass.
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.