| Machine-learning-guided directed evolution for protein engineering KK Yang, Z Wu, FH Arnold Nature methods, 1, 2019 | 579 | 2019 |
| Learned protein embeddings for machine learning KK Yang, Z Wu, CN Bedbrook, FH Arnold Bioinformatics 34 (15), 2642-2648, 2018 | 258 | 2018 |
| Machine learning-guided channelrhodopsin engineering enables minimally invasive optogenetics CN Bedbrook, KK Yang, JE Robinson, ED Mackey, V Gradinaru, ... Nature methods 16 (11), 1176-1184, 2019 | 154 | 2019 |
| Machine learning to design integral membrane channelrhodopsins for efficient eukaryotic expression and plasma membrane localization CN Bedbrook, KK Yang, AJ Rice, V Gradinaru, FH Arnold PLoS computational biology 13 (10), e1005786, 2017 | 103 | 2017 |
| Protein sequence design with deep generative models Z Wu, KE Johnston, FH Arnold, KK Yang Current opinion in chemical biology 65, 18-27, 2021 | 78 | 2021 |
| Signal peptides generated by attention-based neural networks Z Wu, KK Yang, MJ Liszka, A Lee, A Batzilla, D Wernick, DP Weiner, ... ACS Synthetic Biology 9 (8), 2154-2161, 2020 | 55 | 2020 |
| Structure-guided SCHEMA recombination generates diverse chimeric channelrhodopsins CN Bedbrook, AJ Rice, KK Yang, X Ding, S Chen, EM LeProust, ... Proceedings of the National Academy of Sciences 114 (13), E2624-E2633, 2017 | 50 | 2017 |
| Learned embeddings from deep learning to visualize and predict protein sets C Dallago, K Schütze, M Heinzinger, T Olenyi, M Littmann, AX Lu, ... Current Protocols 1 (5), e113, 2021 | 47 | 2021 |
| Evolutionary velocity with protein language models predicts evolutionary dynamics of diverse proteins BL Hie, KK Yang, PS Kim Cell Systems 13 (4), 274-285. e6, 2022 | 42* | 2022 |
| The Generation of Thermostable Fungal Laccase Chimeras by SCHEMA-RASPP Structure-Guided Recombination in Vivo I Mateljak, A Rice, K Yang, T Tron, M Alcalde ACS Synthetic Biology 8 (4), 833-843, 2019 | 34 | 2019 |
| 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 | 33 | 2021 |
| Adaptive machine learning for protein engineering BL Hie, KK Yang Current opinion in structural biology 72, 145-152, 2022 | 32 | 2022 |
| Convolutions are competitive with transformers for protein sequence pretraining KK Yang, N Fusi, AX Lu bioRxiv, 2022.05. 19.492714, 2022 | 24 | 2022 |
| Protein structure generation via folding diffusion KE Wu, KK Yang, R Berg, JY Zou, AX Lu, AP Amini arXiv preprint arXiv:2209.15611, 2022 | 22 | 2022 |
| Machine learning modeling of family wide enzyme-substrate specificity screens S Goldman, R Das, KK Yang, CW Coley PLoS computational biology 18 (2), e1009853, 2022 | 18 | 2022 |
| Batched Stochastic Bayesian Optimization via Combinatorial Constraints Design KK Yang, Y Chen, A Lee, Y Yue arXiv preprint arXiv:1904.08102, 2019 | 18 | 2019 |
| Exploring evolution-based &-free protein language models as protein function predictors M Hu, F Yuan, KK Yang, F Ju, J Su, H Wang, F Yang, Q Ding arXiv preprint arXiv:2206.06583, 2022 | 12 | 2022 |
| Masked inverse folding with sequence transfer for protein representation learning KK Yang, N Zanichelli, H Yeh bioRxiv, 2022.05. 25.493516, 2022 | 9 | 2022 |
| A topological data analytic approach for discovering biophysical signatures in protein dynamics WS Tang, GM da Silva, H Kirveslahti, E Skeens, B Feng, T Sudijono, ... PLoS computational biology 18 (5), e1010045, 2022 | 8 | 2022 |
| Randomized gates eliminate bias in sort‐seq assays BL Trippe, B Huang, EA DeBenedictis, B Coventry, N Bhattacharya, ... Protein Science 31 (9), e4401, 2022 | 2 | 2022 |
Frances ArnoldProfessor of chemical engineering, CaltechVerified email at cheme.caltech.edu
