| A deep learning model for type II polyketide natural product prediction without sequence alignment J Huang, Q Gao, Y Tang, Y Wu, H Zhang, Z Qin Digital Discovery 2 (5), 1484-1493, 2023 | 1 | 2023 |
| An incoherent feedforward loop interprets NFκB/RelA dynamics to determine TNF‐induced necroptosis decisions M Oliver Metzig, Y Tang, S Mitchell, B Taylor, R Foreman, R Wollman, ... Molecular systems biology 16 (12), e9677, 2020 | 20 | 2020 |
| Anomalous free energy changes induced by topology Y Tang, R Yuan, P Ao Physical Review E 92 (6), 062129, 2015 | 9 | 2015 |
| Chemotaxis as a navigation strategy to boost range expansion J Cremer, T Honda, Y Tang, J Wong-Ng, M Vergassola, T Hwa Nature 575 (7784), 658-663, 2019 | 134 | 2019 |
| Comment on “Construction of the landscape for multi-stable systems: Potential landscape, quasi-potential, A-type integral and beyond”[J. Chem. Phys. 144, 094109 (2016)] R Yuan, Y Tang, P Ao The Journal of Chemical Physics 145 (14), 2016 | 1 | 2016 |
| Controlling symmetry-breaking states by a hidden quantity in multiplicative noise Y Tang, R Yuan, J Chen, P Ao Physical Review E 90 (5), 052121, 2014 | 7 | 2014 |
| Decoding early myelopoiesis from dynamics of core endogenous network H Su, G Wang, R Yuan, J Wang, Y Tang, P Ao, X Zhu Science China Life Sciences 60, 627-646, 2017 | 24 | 2017 |
| Dynamical behaviors determined by the Lyapunov function in competitive Lotka-Volterra systems Y Tang, R Yuan, Y Ma Physical Review E—Statistical, Nonlinear, and Soft Matter Physics 87 (1 …, 2013 | 47 | 2013 |
| Escape rate for nonequilibrium processes dominated by strong non-detailed balance force Y Tang, S Xu, P Ao The Journal of Chemical Physics 148 (6), 2018 | 2 | 2018 |
| Free energy amplification by magnetic flux for driven quantum systems Y Tang Communications Physics 4 (1), 9, 2021 | 3 | 2021 |
| Generating transverse response explicitly from harmonic oscillators Y Yao, Y Tang, P Ao Physical Review B 96 (13), 134414, 2017 | 6 | 2017 |
| Learning noise-induced transitions by multi-scaling reservoir computing Z Lin, Z Lu, Z Di, Y Tang Nature Communications 15 (1), 6584, 2024 | 1 | 2024 |
| Learning nonequilibrium statistical mechanics and dynamical phase transitions Y Tang, J Liu, J Zhang, P Zhang Nature Communications 15 (1), 1117, 2024 | 5* | 2024 |
| Neural-network solutions to stochastic reaction networks Y Tang, J Weng, P Zhang Nature Machine Intelligence 5 (4), 376-385, 2023 | 15 | 2023 |
| Nonequilibrium work relation beyond the Boltzmann-Gibbs distribution Y Tang, R Yuan, P Ao Physical Review E 89 (6), 062112, 2014 | 13 | 2014 |
| Optimizing higher-order network topology for synchronization of coupled phase oscillators Y Tang, D Shi, L Lü Communications Physics 5 (1), 96, 2022 | 30 | 2022 |
| Potential landscape of high dimensional nonlinear stochastic dynamics with large noise Y Tang, R Yuan, G Wang, X Zhu, P Ao Scientific reports 7 (1), 15762, 2017 | 44 | 2017 |
| Quantifying information accumulation encoded in the dynamics of biochemical signaling Y Tang, A Adelaja, FXF Ye, E Deeds, R Wollman, A Hoffmann Nature communications 12 (1), 1272, 2021 | 31 | 2021 |
| Quantifying information of intracellular signaling: progress with machine learning Y Tang, A Hoffmann Reports on Progress in Physics 85 (8), 086602, 2022 | 15 | 2022 |
| Reversed graph embedding resolves complex single-cell trajectories X Qiu, Q Mao, Y Tang, L Wang, R Chawla, HA Pliner, C Trapnell Nature methods 14 (10), 979-982, 2017 | 3174 | 2017 |
Ruoshi YuanDepartment of Bioengineering at the University of California, Berkeley在 berkeley.edu 的电子邮件经过验证
