View in EDS

LevSeq: Rapid Generation of Sequence-Function Data for Directed Evolution and Machine Learning.

Saved in:

Bibliographic Details
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

Description
ISSN:	2161-5063
DOI:	10.1021/acssynbio.4c00625