| Deep direct reinforcement learning for financial signal representation and trading Y Deng, F Bao, Y Kong, Z Ren, Q Dai IEEE transactions on neural networks and learning systems 28 (3), 653-664, 2016 | 843 | 2016 |
| Giotto: a toolbox for integrative analysis and visualization of spatial expression data R Dries, Q Zhu, R Dong, CHL Eng, H Li, K Liu, Y Fu, T Zhao, A Sarkar, ... Genome biology 22, 1-31, 2021 | 496 | 2021 |
| A hierarchical fused fuzzy deep neural network for data classification Y Deng, Z Ren, Y Kong, F Bao, Q Dai IEEE Transactions on Fuzzy Systems 25 (4), 1006-1012, 2016 | 366 | 2016 |
| Cooperative deep reinforcement learning for large-scale traffic grid signal control T Tan, F Bao, Y Deng, A Jin, Q Dai, J Wang IEEE transactions on cybernetics 50 (6), 2687-2700, 2019 | 212 | 2019 |
| Scalable analysis of cell-type composition from single-cell transcriptomics using deep recurrent learning Y Deng, F Bao, Q Dai, LF Wu, SJ Altschuler Nature methods 16 (4), 311-314, 2019 | 143 | 2019 |
| Unsupervised content-preserving transformation for optical microscopy X Li, G Zhang, H Qiao, F Bao, Y Deng, J Wu, Y He, J Yun, X Lin, H Xie, ... Light: Science & Applications 10 (1), 44, 2021 | 79 | 2021 |
| Sparse coding-inspired optimal trading system for HFT industry Y Deng, Y Kong, F Bao, Q Dai IEEE Transactions on Industrial Informatics 11 (2), 467-475, 2015 | 62 | 2015 |
| Integrative spatial analysis of cell morphologies and transcriptional states with MUSE F Bao, Y Deng, S Wan, SQ Shen, B Wang, Q Dai, SJ Altschuler, LF Wu Nature biotechnology 40 (8), 1200-1209, 2022 | 58 | 2022 |
| Deep learning model for ultrafast multifrequency optical property extractions for spatial frequency domain imaging Y Zhao, Y Deng, F Bao, H Peterson, R Istfan, D Roblyer Optics letters 43 (22), 5669-5672, 2018 | 53 | 2018 |
| Giotto, a pipeline for integrative analysis and visualization of single-cell spatial transcriptomic data R Dries, Q Zhu, CHL Eng, A Sarkar, F Bao, RE George, N Pierson, L Cai, ... BioRxiv, 701680, 2019 | 41 | 2019 |
| Learning Deep Landmarks for Imbalanced Classification F Bao, Y Deng, Y Kong, Z Ren, J Suo, Q Dai IEEE Transactions on Neural Networks and Learning Systems, 2019 | 35 | 2019 |
| Deep and structured robust information theoretic learning for image analysis Y Deng, F Bao, X Deng, R Wang, Y Kong, Q Dai IEEE Transactions on Image Processing 25 (9), 4209-4221, 2016 | 35 | 2016 |
| Human-in-the-loop low-shot learning S Wan, Y Hou, F Bao, Z Ren, Y Dong, Q Dai, Y Deng IEEE transactions on neural networks and learning systems 32 (7), 3287-3292, 2020 | 19 | 2020 |
| Massive single-cell RNA-seq analysis and imputation via deep learning Y Deng, F Bao, Q Dai, LF Wu, SJ Altschuler BioRxiv, 315556, 2018 | 19 | 2018 |
| Bosco: Boosting corrections for genome-wide association studies with imbalanced samples F Bao, Y Deng, Y Zhao, J Suo, Q Dai IEEE Transactions on NanoBioscience 16 (1), 69-77, 2017 | 15 | 2017 |
| ACID: association correction for imbalanced data in GWAS F Bao, Y Deng, Q Dai IEEE/ACM Transactions on Computational Biology and Bioinformatics 15 (1 …, 2016 | 12 | 2016 |
| Disentangled variational information bottleneck for multiview representation learning F Bao Artificial Intelligence: First CAAI International Conference, CICAI 2021 …, 2021 | 9 | 2021 |
| Probabilistic natural mapping of gene-level tests for genome-wide association studies F Bao, Y Deng, M Du, Z Ren, Q Zhang, Y Zhao, J Suo, Z Zhang, M Wang, ... Briefings in bioinformatics 19 (4), 545-553, 2018 | 9 | 2018 |
| Information transduction capacity reduces the uncertainties in annotation-free isoform discovery and quantification Y Deng, F Bao, Y Yang, X Ji, M Du, Z Zhang, M Wang, Q Dai Nucleic acids research 45 (15), e143-e143, 2017 | 7 | 2017 |
| Explaining the genetic causality for complex phenotype via deep association kernel learning F Bao, Y Deng, M Du, Z Ren, S Wan, KY Liang, S Liu, B Wang, J Xin, ... Patterns 1 (6), 2020 | 6 | 2020 |
Qionghai DaiTsinghua University在 tsinghua.edu.cn 的电子邮件经过验证
