LevSeq: Rapid Generation of Sequence-Function Data for Directed Evolution and Machine Learning.
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| Title: | LevSeq: Rapid Generation of Sequence-Function Data for Directed Evolution and Machine Learning. |
|---|---|
| Authors: | Long Y; Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, California91125, United States., Mora A; Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, California91125, United States., Li FZ; Division of Biology and Bioengineering, California Institute of Technology, Pasadena, California91125, United States., Gürsoy E; Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, California91125, United States., Johnston KE; Division of Biology and Bioengineering, California Institute of Technology, Pasadena, California91125, United States., Arnold FH; Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, California91125, United States.; Division of Biology and Bioengineering, California Institute of Technology, Pasadena, California91125, United States. |
| Source: | ACS synthetic biology [ACS Synth Biol] 2025 Jan 17; Vol. 14 (1), pp. 230-238. Date of Electronic Publication: 2024 Dec 24. |
| Publication Type: | Journal Article; Research Support, Non-U.S. Gov't; Research Support, U.S. Gov't, Non-P.H.S. |
| Journal Info: | Publisher: American Chemical Society Country of Publication: United States NLM ID: 101575075 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2161-5063 (Electronic) Linking ISSN: 21615063 NLM ISO Abbreviation: ACS Synth Biol Subsets: MEDLINE |
| Database: | MEDLINE Ultimate |
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