Research at FAST

103 Department of Applied Mathematics Department of Applied Mathematics Qualification BSc (HKU) MPhil (HKU) PhD (UNC-Chapel Hill) ORCID ID 0000-0001-9066-1619 Representative Publications • Wong, K. Y. , Fan, C., Tanioka, M., Parker, J. S., Nobel, A. B., Zeng, D., Lin, D. Y., and Perou, C. M. ( 2019 ). I-Boost: An Integrative Boosting Approach for Predicting Survival Time With Multiple Genomics Platforms. Genome Biol., 20, 52 • Wong, K. Y. , Zeng, D., and Lin, D. Y. ( 2019 ). Robust Score Tests With Missing Data in Genomics Studies. J. Am. Statist. Ass., 114: 1778–1786 Dr WONG Kin Yau Alex Assistant Professor Research Overview Recent technological advances have made it possible to collect different types of genomic data, including DNA copy number, SNP genotype, DNA methylation level, and expression levels of mRNA, microRNA, and protein, on a large number of subjects. There is a growing interest in integrating these genomic platforms so as to understand their biological relationships and predict disease outcomes. Integrative analysis of genomic data is challenging for various reasons. First, the relationships among different genomic variables are complex; see the figure for the potential relationships among variables. Second, each type of genomic data is high-dimensional. Third, there are substantial missing data in large-scale genomic studies. Novel statistical methods that accommodate the complexity of the data are needed. We aim at developing methods with sound theoretical justifications for identifying genomic variables associated with phenotypes from multi-platform genomic data. We study the association analysis between phenotypes and a set of incomplete genomic variables. We also develop methods for prediction of disease outcomes using all types of genomic variables. The research will advance the understanding of complex diseases and also contribution to the studies of high- dimensional data analysis, analysis of incomplete data, semiparametric methods, and survival analysis. • Wong, K. Y. , Zeng, D., and Lin, D. Y. ( 2018 ). Efficient Estimation for Semiparametric Structural Equation Models With Censored Data. J. Am. Statist. Ass., 113: 893–905 • Wong, K. Y. , Goldberg, Y., and Fine, J. P. ( 2016 ). Oracle Estimation of Parametric Models Under Boundary Constraints. Biometrics, 72:1173–1183