| Machine-learning-guided directed evolution for protein engineering KK Yang, Z Wu, FH Arnold Nature methods, 1, 2019 | 1021 | 2019 |
| Learned protein embeddings for machine learning KK Yang, Z Wu, CN Bedbrook, FH Arnold Bioinformatics 34 (15), 2642-2648, 2018 | 366 | 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 | 230 | 2019 |
| Protein structure generation via folding diffusion KE Wu, KK Yang, R van den Berg, S Alamdari, JY Zou, AX Lu, AP Amini Nature communications 15 (1), 1059, 2024 | 171 | 2024 |
| Protein sequence design with deep generative models Z Wu, KE Johnston, FH Arnold, KK Yang Current opinion in chemical biology 65, 18-27, 2021 | 153 | 2021 |
| Three-dimensional fibrous scaffolds with microstructures and nanotextures for tissue engineering R Ng, R Zang, KK Yang, N Liu, ST Yang Rsc Advances 2 (27), 10110-10124, 2012 | 152 | 2012 |
| 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 | 143 | 2021 |
| 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 | 136 | 2017 |
| Convolutions are competitive with transformers for protein sequence pretraining KK Yang, N Fusi, AX Lu Cell Systems 15 (3), 286-294. e2, 2024 | 118 | 2024 |
| 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 | 118* | 2022 |
| Protein generation with evolutionary diffusion: sequence is all you need S Alamdari, N Thakkar, R van den Berg, N Tenenholtz, B Strome, ... BioRxiv, 2023.09. 11.556673, 2023 | 105* | 2023 |
| 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 | 102 | 2020 |
| Adaptive machine learning for protein engineering BL Hie, KK Yang Current opinion in structural biology 72, 145-152, 2022 | 99 | 2022 |
| 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 | 91 | 2021 |
| 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 | 83 | 2022 |
| Masked inverse folding with sequence transfer for protein representation learning KK Yang, N Zanichelli, H Yeh Protein Engineering, Design and Selection 36, gzad015, 2023 | 80 | 2023 |
| 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 | 62 | 2017 |
| Exploring evolution-aware &-free protein language models as protein function predictors M Hu, F Yuan, K Yang, F Ju, J Su, H Wang, F Yang, Q Ding Advances in Neural Information Processing Systems 35, 38873-38884, 2022 | 52 | 2022 |
| Computational scoring and experimental evaluation of enzymes generated by neural networks SR Johnson, X Fu, S Viknander, C Goldin, S Monaco, A Zelezniak, ... Nature biotechnology, 1-10, 2024 | 44 | 2024 |
| 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 | 44 | 2019 |
Frances ArnoldProfessor of chemical engineering, CaltechPotvrđena adresa e-pošte na cheme.caltech.edu