Inchi Hu
Affiliations: | Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong |
Area:
StatisticsGoogle:
"Inchi Hu"Children
Sign in to add traineeKwok W. Ho | grad student | 2005 | HKUST |
Haitian Wang | grad student | 2011 | HKUST |
BETA: Related publications
See more...
Publications
You can help our author matching system! If you notice any publications incorrectly attributed to this author, please sign in and mark matches as correct or incorrect. |
Wang MH, Chang B, Sun R, et al. (2017) Stratified polygenic risk prediction model with application to CAGI bipolar disorder sequencing data. Human Mutation |
Sun R, Deng Q, Hu I, et al. (2016) A clustering approach to identify rare variants associated with hypertension. Bmc Proceedings. 10: 153-157 |
Sun R, Weng H, Hu I, et al. (2016) A W-test collapsing method for rare-variant association testing in exome sequencing data. Genetic Epidemiology |
Wang MH, Sun R, Guo J, et al. (2016) A fast and powerful W-test for pairwise epistasis testing. Nucleic Acids Research |
Agne M, Huang CH, Hu I, et al. (2014) Considering interactive effects in the identification of influential regions with extremely rare variants via fixed bin approach. Bmc Proceedings. 8: S7 |
Wang MH, Huang CH, Zheng T, et al. (2014) Discovering pure gene-environment interactions in blood pressure genome-wide association studies data: a two-step approach incorporating new statistics. Bmc Proceedings. 8: S62 |
Fan R, Huang CH, Hu I, et al. (2014) A partition-based approach to identify gene-environment interactions in genome wide association studies. Bmc Proceedings. 8: S60 |
Liu Y, Huang C, Hu I, et al. (2014) A dual-clustering framework for association screening with whole genome sequencing data and longitudinal traits. Bmc Proceedings. 8: S47 |
Wang H, Lo SH, Zheng T, et al. (2012) Interaction-based feature selection and classification for high-dimensional biological data. Bioinformatics (Oxford, England). 28: 2834-42 |
Liu Y, Huang CH, Hu I, et al. (2011) Association screening for genes with multiple potentially rare variants: an inverse-probability weighted clustering approach. Bmc Proceedings. 5: S106 |