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 | 555 | 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 | 401 | 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 | 326 | 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 | 260 | 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 | 258 | 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 | 51* | 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 | 42 | 2019 |
Channel Normalization in Convolutional Neural Network avoids Vanishing Gradients Z Dai, R Heckel | 32 | 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 |
Multi-cohort analysis of colorectal cancer metagenome identified altered bacteria across populations and universal bacterial markers. Microbiome. 2018; 6 (1): 70 Z Dai, OO Coker, G Nakatsu, WKK Wu, L Zhao, Z Chen, FKL Chan, ... DOI: https://doi. org/10.1186/s40168-018-0451-2, 70, 0 | 32 | |
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 | 11 | 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 |
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 | 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 |
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 | | |