首页|University of Colorado Boulder Reports Findings in Machine Learning (SeqImprove: Machine-Learning-Assisted Curation of Genetic Circuit Sequence Information)
University of Colorado Boulder Reports Findings in Machine Learning (SeqImprove: Machine-Learning-Assisted Curation of Genetic Circuit Sequence Information)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Machine Learning is th e subject of a report. According to news reporting originating from Boulder, Col orado, by NewsRx correspondents, research stated, “The progress and utility of s ynthetic biology is currently hindered by the lengthy process of studying litera ture and replicating poorly documented work. Reconstruction of crucial design in formation through post hoc curation is highly noisy and error-prone.” Our news editors obtained a quote from the research from the University of Color ado Boulder, “To combat this, author participation during the curation process i s crucial. To encourage author participation without overburdening them, an ML-a ssisted curation tool called SeqImprove has been developed. Using named entity r ecognition, called entity normalization, and sequence matching, SeqImprove creat es machine-accessible sequence data and metadata annotations, which authors can then review and edit before submitting a final sequence file.” According to the news editors, the research concluded: “SeqImprove makes it easi er for authors to submit sequence data that is FAIR (findable, accessible, inter operable, and reusable).” This research has been peer-reviewed.
BoulderColoradoUnited StatesNorth and Central AmericaCyborgsEmerging TechnologiesGeneticsMachine Learning
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