Kevin K. Yang
Kevin K. Yang
Microsoft Research
Verified email at - Homepage
Cited by
Cited by
Machine-learning-guided directed evolution for protein engineering
KK Yang, Z Wu, FH Arnold
Nature methods, 1, 2019
Learned protein embeddings for machine learning
KK Yang, Z Wu, CN Bedbrook, FH Arnold
Bioinformatics 34 (15), 2642-2648, 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
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
Protein sequence design with deep generative models
Z Wu, KE Johnston, FH Arnold, KK Yang
Current opinion in chemical biology 65, 18-27, 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
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
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
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
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
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
Adaptive machine learning for protein engineering
BL Hie, KK Yang
Current opinion in structural biology 72, 145-152, 2022
Convolutions are competitive with transformers for protein sequence pretraining
KK Yang, N Fusi, AX Lu
bioRxiv, 2022.05. 19.492714, 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
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
Batched Stochastic Bayesian Optimization via Combinatorial Constraints Design
KK Yang, Y Chen, A Lee, Y Yue
arXiv preprint arXiv:1904.08102, 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
Masked inverse folding with sequence transfer for protein representation learning
KK Yang, N Zanichelli, H Yeh
bioRxiv, 2022.05. 25.493516, 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
Randomized gates eliminate bias in sort‐seq assays
BL Trippe, B Huang, EA DeBenedictis, B Coventry, N Bhattacharya, ...
Protein Science 31 (9), e4401, 2022
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