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Artificial intelligence for synthetic biology

Published:2022-04 Issue:5 Volume:65 Page:88-97
ISSN:0001-0782
Container-title:Communications of the ACM
language:en
Short-container-title:Commun. ACM

Author:

Eslami Mohammed¹,Adler Aaron²,Caceres Rajmonda S.³,Dunn Joshua G.⁴,Kelley-Loughnane Nancy⁵,Varaljay Vanessa A.⁶,Martin Hector Garcia⁷

Affiliation:

1. Netrias, LLC, Arlington, VA

2. Raytheon BBN, Columbia, MD

3. MIT Lincoln Laboratory, Lexington, MA

4. Ginkgo Bioworks, Boston, MA

5. Air Force Research Laboratory, Wright Patterson Air Force Base, OH

6. Air Force Research Laboratory, Wright-Patterson, OH

7. Lawrence Berkeley National Laboratory

Abstract

The opportunities and challenges of adapting and applying AI principles to synbio.

Publisher

Association for Computing Machinery (ACM)

Subject

General Computer Science

Link

https://dl.acm.org/doi/pdf/10.1145/3500922

Reference39 articles.

1. Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning

2. The Eugene Language for Synthetic Biology

3. A brief history of synthetic biology

4. Opportunities at the Intersection of Synthetic Biology, Machine Learning, and Automation

5. Automated “Cells-To-Peptides” Sample Preparation Workflow for High-Throughput, Quantitative Proteomic Assays of Microbes

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5. Unlocking the Transformative Power of Synthetic Biology;Archives of Biotechnology and Biomedicine;2024-04-18

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