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Kadina E. Johnston
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Protein sequence design with deep generative models
Z Wu, KE Johnston, FH Arnold, KK Yang
Current Opinion in Chemical Biology 65, 18-27, 2021
1272021
Advances in machine learning for directed evolution
BJ Wittmann, KE Johnston, Z Wu, FH Arnold
Current Opinion in Structural Biology 69, 11-18, 2021
1202021
FLIP: Benchmark tasks in fitness landscape inference for proteins
C Dallago, J Mou, KE Johnston, BJ Wittmann, N Bhattacharya, S Goldman, ...
bioRxiv, 2021.11. 09.467890, 2021
802021
evSeq: Cost-Effective Amplicon Sequencing of Every Variant in a Protein Library
BJ Wittmann, KE Johnston, PJ Almhjell, FH Arnold
ACS Synthetic Biology 11 (3), 1313-1324, 2022
302022
Machine Learning for Protein Engineering
KE Johnston, C Fannjiang, BJ Wittmann, BL Hie, KK Yang, Z Wu
arXiv preprint arXiv:2305.16634, 2023
62023
DeCOIL: Optimization of Degenerate Codon Libraries for Machine Learning-Assisted Protein Engineering
J Yang, J Ducharme, KE Johnston, FZ Li, Y Yue, FH Arnold
bioRxiv, 2023.05. 11.540424, 2023
62023
Double-Network Nanogel as a Nonviral Vector for DNA Delivery
M Ye, Y Wang, Y Zhao, R Xie, N Yodsanit, K Johnston, S Gong
ACS applied materials & interfaces 11 (46), 42865-42872, 2019
62019
Directed evolution of enzymatic silicon-carbon bond cleavage in siloxanes
NS Sarai, TJ Fulton, RL O’Meara, KE Johnston, S Brinkmann-Chen, ...
Science 383 (6681), 438-443, 2024
52024
Designing a compact, low-cost FRET microscopy platform for the undergraduate classroom
JW Rupel, SM Sdao, KE Johnston, ET Nethery, KA Gabardi, BA Ratliff, ...
The Biophysicist 1 (2), 4, 2020
12020
Acquiring Enzyme Sequence-Fitness Data at Scale Toward Predictive Methods for Enzyme Engineering
KE Johnston
California Institute of Technology, 2024
2024
Machine Learning in Molecular Sciences
C Qu, H Liu
Springer Nature, 2023
2023
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Articles 1–11