| Mucosal microbiome dysbiosis in gastric carcinogenesis OO Coker, Z Dai, Y Nie, G Zhao, L Cao, G Nakatsu, WKK Wu, SH Wong, ... Gut 67 (6), 1024-1032, 2018 | 532 | 2018 |
| Multi-cohort analysis of colorectal cancer metagenome identified altered bacteria across populations and universal bacterial markers Z Dai, OO Coker, G Nakatsu, WKK Wu, L Zhao, Z Chen, FKL Chan, ... Microbiome 6, 1-12, 2018 | 397 | 2018 |
| Alterations in enteric virome are associated with colorectal cancer and survival outcomes G Nakatsu, H Zhou, WKK Wu, SH Wong, OO Coker, Z Dai, X Li, CH Szeto, ... Gastroenterology 155 (2), 529-541. e5, 2018 | 315 | 2018 |
| Association between bacteremia from specific microbes and subsequent diagnosis of colorectal cancer TNY Kwong, X Wang, G Nakatsu, TC Chow, T Tipoe, RZW Dai, KKK Tsoi, ... Gastroenterology 155 (2), 383-390. e8, 2018 | 255 | 2018 |
| Quantitation of faecal Fusobacterium improves faecal immunochemical test in detecting advanced colorectal neoplasia SH Wong, TNY Kwong, TC Chow, AKC Luk, RZW Dai, G Nakatsu, ... Gut 66 (8), 1441-1448, 2017 | 254 | 2017 |
| Adaptive learned bloom filter (ada-bf): Efficient utilization of the classifier with application to real-time information filtering on the web Z Dai, A Shrivastava Advances in neural information processing systems 33, 11700-11710, 2020 | 50* | 2020 |
| Batch effects correction for microbiome data with Dirichlet-multinomial regression Z Dai, SH Wong, J Yu, Y Wei Bioinformatics 35 (5), 807-814, 2019 | 41 | 2019 |
| Disease burden of Clostridium difficile infections in adults, Hong Kong, China, 2006–2014 J Ho, RZW Dai, TNY Kwong, X Wang, L Zhang, M Ip, R Chan, ... Emerging infectious diseases 23 (10), 1671, 2017 | 32 | 2017 |
| Channel Normalization in Convolutional Neural Network avoids Vanishing Gradients Z Dai, R Heckel | 31 | 2019 |
| Oncogenes without a neighboring tumor-suppressor gene are more prone to amplification WKK Wu, X Li, X Wang, RZW Dai, ASL Cheng, MHT Wang, T Kwong, ... Molecular Biology and Evolution 34 (4), 903-907, 2017 | 10 | 2017 |
| Active sampling count sketch (ascs) for online sparse estimation of a trillion scale covariance matrix Z Dai, A Desai, R Heckel, A Shrivastava Proceedings of the 2021 International Conference on Management of Data, 352-364, 2021 | 6 | 2021 |
| Optimizing learned bloom filters: How much should be learned? Z Dai, A Shrivastava, P Reviriego, JA Hernández IEEE Embedded Systems Letters 14 (3), 123-126, 2022 | 5 | 2022 |
| Learned bloom filters in adversarial environments: a malicious URL detection use-case P Reviriego, JA Hernández, Z Dai, A Shrivastava 2021 IEEE 22nd International Conference on High Performance Switching and …, 2021 | 5 | 2021 |
| Federated multiple label hashing (fedmlh): Communication efficient federated learning on extreme classification tasks Z Dai, C Dun, Y Tang, A Kyrillidis, A Shrivastava arXiv preprint arXiv:2110.12292, 2021 | 2 | 2021 |
| Graph Self-supervised Learning via Proximity Distribution Minimization T Zhang, Z Dai, Z Xu, A Shrivastava Uncertainty in Artificial Intelligence, 2498-2508, 2023 | | 2023 |
| Memory efficient computation for large scale machine learning and data inference Z Dai RICE UNIVERSITY, 2022 | | 2022 |
| ScatterSample: Diversified Label Sampling for Data Efficient Graph Neural Network Learning Z Dai, V Ioannidis, S Adeshina, Z Jost, C Faloutsos, G Karypis arXiv preprint arXiv:2206.04255, 2022 | | 2022 |
| 87-Enteric Fungi Compositional and Ecological Dysbiosis in Colorectal Cancer OO Coker, G Nakatsu, Z Dai, WK Wu, SH Wong, FK Chan, JJ Sung, J Yu Gastroenterology 154 (6), S-25, 2018 | | 2018 |
| Graph Self-supervised Learning via Proximity Distribution Minimization (Supplementary Material) T Zhang, Z Dai, Z Xu, A Shrivastava PDM (heat kernel) 84 (74.3), 83.6, 0 | | |
Sunny H. WongLee Kong Chian School of MedicineDirección de correo verificada de ntu.edu.sg
