SeqImprove: Machine-Learning-Assisted Curation of Genetic Circuit Sequence Information-Reference-Cited by-同舟云学术

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SeqImprove: Machine-Learning-Assisted Curation of Genetic Circuit Sequence Information

Published:2024-09-04 Issue: Volume: Page:
ISSN:2161-5063
Container-title:ACS Synthetic Biology
language:en
Short-container-title:ACS Synth. Biol.

Author:

Mante Jeanet¹^ORCID,Sents Zach¹^ORCID,Britt Duncan¹,Mo William¹^ORCID,Liao Chunxiao¹,Greer Ryan¹,Myers Chris J.¹^ORCID

Affiliation:

1. University of Colorado Boulder, Boulder, Colorado 80309, United States

Funder

Division of Molecular and Cellular Biosciences

Charles Stark Draper Laboratory

Office of Advanced Cyberinfrastructure

Publisher

American Chemical Society (ACS)

Link

https://pubs.acs.org/doi/pdf/10.1021/acssynbio.4c00392

Reference23 articles.

1. The challenges of informatics in synthetic biology: from biomolecular networks to artificial organisms

2. iGEM 2.0—refoundations for engineering biology

3. Advancing reuse of genetic parts: progress and remaining challenges

4. The future of biocuration

5. The FAIR Guiding Principles for scientific data management and stewardship

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