Zhenwei DAI
Zhenwei DAI
PhD, Department of Statistics, Rice University
Verified email at
Cited by
Cited by
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
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
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
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
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
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
Batch effects correction for microbiome data with Dirichlet-multinomial regression
Z Dai, SH Wong, J Yu, Y Wei
Bioinformatics 35 (5), 807-814, 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
Channel Normalization in Convolutional Neural Network avoids Vanishing Gradients
Z Dai, R Heckel
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
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
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
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
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
Graph Self-supervised Learning via Proximity Distribution Minimization
T Zhang, Z Dai, Z Xu, A Shrivastava
Uncertainty in Artificial Intelligence, 2498-2508, 2023
Memory efficient computation for large scale machine learning and data inference
Z Dai
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
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
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
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