@misc{indiciaeda3f1c39332c,
  title = {LevSeq: Rapid Generation of Sequence-Function Data for Directed Evolution and Machine Learning},
  author = {Long, Yueming [California Institute of Technology (CalTech), Pasadena, CA (United States)] (ORCID:0009000651127791) and Mora, Ariane [California Institute of Technology (CalTech), Pasadena, CA (United States)] and Li, Francesca-Zhoufan [California Institute of Technology (CalTech), Pasadena, CA (United States)] (ORCID:0000000257109512) and Gürsoy, Emre [California Institute of Technology (CalTech), Pasadena, CA (United States); ETH Zurich, Basel (Switzerland)] and Johnston, Kadina E. [California Institute of Technology (CalTech), Pasadena, CA (United States); Merck \& Co., Inc., South San Francisco, CA (United States)] (ORCID:0000000222143534) and Arnold, Frances H. [California Institute of Technology (CalTech), Pasadena, CA (United States)] (ORCID:000000024027364X)},
  year = {2024},
  doi = {10.1021/acssynbio.4c00625},
  url = {https://www.osti.gov/biblio/2567050},
  note = {Source identifier: 2567050}
}