| New interpretable deep learning model to monitor real-time PM2. 5 concentrations from satellite data X Yan, Z Zang, N Luo, Y Jiang, Z Li Environment International 144, 106060, 2020 | 90 | 2020 |
| A Spatial-Temporal Interpretable Deep Learning Model for improving interpretability and predictive accuracy of satellite-based PM2. 5 X Yan, Z Zang, Y Jiang, W Shi, Y Guo, D Li, C Zhao, L Husi Environmental Pollution 273, 116459, 2021 | 86 | 2021 |
| Structure-aware multimodal deep learning for drug–protein interaction prediction P Wang, S Zheng, Y Jiang, C Li, J Liu, C Wen, A Patronov, D Qian, ... Journal of chemical information and modeling 62 (5), 1308-1317, 2022 | 53 | 2022 |
| A deep learning approach to improve the retrieval of temperature and humidity profiles from a ground-based microwave radiometer X Yan, C Liang, Y Jiang, N Luo, Z Zang, Z Li IEEE transactions on geoscience and remote sensing 58 (12), 8427-8437, 2020 | 50 | 2020 |
| Tree-based ensemble deep learning model for spatiotemporal surface ozone (O3) prediction and interpretation Z Zang, Y Guo, Y Jiang, C Zuo, D Li, W Shi, X Yan International Journal of Applied Earth Observation and Geoinformation 103 …, 2021 | 25 | 2021 |
| Cooperative simultaneous inversion of satellite-based real-time PM2. 5 and ozone levels using an improved deep learning model with attention mechanism X Yan, C Zuo, Z Li, HW Chen, Y Jiang, B He, H Liu, J Chen, W Shi Environmental pollution 327, 121509, 2023 | 22 | 2023 |
| Explainable and spatial dependence deep learning model for satellite-based O3 monitoring in China N Luo, Z Zang, C Yin, M Liu, Y Jiang, C Zuo, W Zhao, W Shi, X Yan Atmospheric Environment 290, 119370, 2022 | 22 | 2022 |
| A global land aerosol fine-mode fraction dataset (2001–2020) retrieved from MODIS using hybrid physical and deep learning approaches X Yan, Z Zang, Z Li, N Luo, C Zuo, Y Jiang, D Li, Y Guo, W Zhao, W Shi, ... Earth System Science Data Discussions 2021, 1-27, 2021 | 21 | 2021 |
| Evaluation of four meteorological reanalysis datasets for satellite-based PM2. 5 retrieval over China C Zuo, J Chen, Y Zhang, Y Jiang, M Liu, H Liu, W Zhao, X Yan Atmospheric Environment 305, 119795, 2023 | 18 | 2023 |
| Combining physical mechanisms and deep learning models for hourly surface ozone retrieval in China X Yan, Y Guo, Y Zhang, J Chen, Y Jiang, C Zuo, W Zhao, W Shi Journal of Environmental Management 351, 119942, 2024 | 6 | 2024 |
| Unveiling global land fine-and coarse-mode aerosol dynamics from 2005 to 2020 using enhanced satellite-based monthly inversion data N Luo, Y Zhang, Y Jiang, C Zuo, J Chen, W Zhao, W Shi, X Yan Environmental Pollution 348, 123838, 2024 | 4 | 2024 |
| X-DPI: A structure-aware multi-modal deep learning model for drug-protein interactions prediction P Wang, S Zheng, Y Jiang, C Li, J Liu, C Wen, A Patronov, D Qian, ... bioRxiv, 2021.06. 17.448780, 2021 | 3 | 2021 |
| Substantial Underestimation of Fine-Mode Aerosol Loading from Wildfires and Its Radiative Effects in Current Satellite-Based Retrievals over the United States X Yan, C Zuo, Z Li, HW Chen, Y Jiang, Q Wang, G Wang, K Jia, Z Chen, ... Environmental Science & Technology 58 (35), 15661-15671, 2024 | | 2024 |
| Deep Learning with Pretrained Framework Unleashes the Power of Satellite-Based Global Fine-Mode Aerosol Retrieval X Yan, Z Zang, Z Li, HW Chen, J Chen, Y Jiang, Y Chen, B He, C Zuo, ... Environmental Science & Technology 58 (32), 14260-14270, 2024 | | 2024 |
| Spectral Deconvolution Algorithm for Global Fine-Mode Aerosol Retrieval in the 1990s Using Dual-Angle Satellite Aerosol Data X Yan, J Chen, C Zuo, Y Jiang, B He IEEE Transactions on Geoscience and Remote Sensing 61, 1-11, 2023 | | 2023 |
| Cooperative simultaneous inversion of satellite-based real-time PM2. 5 and ozone X Yan, C Zuo, Z Li, HW Chen, Y Jiang, B He, H Liu, J Chen, W Shi | | |
Xing YanBeijing Normal UniversityVerified email at bnu.edu.cn