Genome wide association studies for complex traits
M. Chen, J. Cho, H. Zhao (2011) Incorporating biological pathways via a Markov random field model in genome-wide association studies. PLoS Genetics, 7: e1001353.
L. Hou, M. Chen, C. K. Zhang, J. Cho, H. Zhao (2014) Guilt by Rewiring: Gene prioritization through network rewiring in genome wide association studies. Human Molecular Genetics, 23: 2780-2790.
D. Chung, C. Yang, C. Li, J. Gelernter, H. Zhao (2014) GPA: A statistical approach to prioritizing GWAS results by integrating pleiotropy and annotation. PLOS Genetics, 10: e1004787.
Q. Wang, C. Yang, J. Gelernter, H. Zhao (2015) Pervasive pleiotropy between psychiatric disorders and immune disorders revealed by integrative analysis of multiple GWAS. Human Genetics, 134: 1195-1209.
Q. Lu, R. Powles, Q. Wang, J. He, H. Zhao (2016) Integrative tissue-specific functional annotations in the human genome provide novel insights on many complex traits and improve signal prioritization in genome wide association studies. PLOS Genetics, 12: e1005947.
J. Jiang, C. Li, D. Paul, C. Yang, H. Zhao (2016) On high-dimensional misspecified mixed model analysis in genome-wide association study. Annals of Statistics, 44: 2127–2160.
 Disease risk predictions
J. Kang, J. Cho, H. Zhao (2010) Practical issues in building risk predicting models for complex diseases. Journal of Biopharmaceutical Statistics, 20: 415-440.
C. Li, C. Yang, J. Gelernter, H. Zhao (2014) Improving genetic risk prediction by leveraging pleiotropy. Human Genetics, 133: 639-650.
G. Li, Y. Cui, H. Zhao (2015) An Empirical Bayes risk prediction model using multiple traits for sequencing data. Statistical Applications in Genetics and Molecular Biology, 14: 551-573.
 Cancer genomics
X. Chen, F. J. Slack, H. Zhao (2013) Joint analysis of expression profiles from multiple cancers improves the identification of microRNA-gene interactions. Bioinformatics, 29: 2137-2145.
M. Chen, M. Gunel, H. Zhao (2013) SomatiCA: Identifying, Characterizing and Quantifying Somatic Copy Number Aberrations from Cancer Genome Sequencing Data. PLoS One, 8: e78143.
G. Ryslik, Y. Cheng, Y. Modis. H. Zhao (2016) Leveraging protein quaternary structure to identify oncogenic driver mutations. BMC Bioinformatics, 17: 137.
X. Huang, D. Stern, H. Zhao (2016) Transcriptional profiles from paired normal samples offer complementary information on cancer patient survival — Evidence from TCGA Pan-Cancer Data. Scientific Reports, 6: 20567.
 Human brain transcriptomes
Z. Lin, S. Sanders, M. Li, N. Sestan, M. State, H. Zhao (2015) A Markov random field-based approach to characterizing human brain development using spatial-temporal transcriptome data. Annals of Applied Statistics, 9: 429–451.
 Drug target identifications
H. Ma, H. Zhao (2012) iFad: an integrative factor analysis model for drug-pathway association inference. Bioinformatics, 28: 1911-1918.
H. Ma, H. Zhao (2012) FacPad: Bayesian sparse factor modeling for the inference of pathways responsive to drug treatment. Bioinformatics, 28: 2662-2670.
C. Li, C. Yang, G. Hather, R. Liu, H. Zhao (2016) Efficient drug-pathway association analysis via integrative penalized matrix decomposition. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 13: 531-540.
Y. Liu, H. Zhao (2016) Predicting synergistic effects between compounds through their structural similarity and effects on transcriptomes. Bioinformatics, in press.
 Graphical models
R. Luo, H. Zhao (2011) Bayesian hierarchical modeling for signaling pathway inference from single cell interventional data. Annals of Applied Statistics, 5: 725–745.
B. Li, H. Chun, H. Zhao (2012) Sparse estimation of conditional graphical models with application to gene networks. Journal of American Statistical Association, 107: 152-167.
B. Li, H. Chun, H. Zhao (2014) On an additive semi-graphoid model for statistical networks with application to pathway analysis. Journal of American Statistical Association, 109: 1188-1204.
H. Chun, X. Zhang, H. Zhao (2015) Gene regulation network inference with joint sparse Gaussian graphical models. Journal of Computational and Graphical Statistics, 24: 954–974.
K. Lee, B. Li, H. Zhao (2016) On an additive partial correlation operator and nonparametric estimation of graphical models. Biometrika, in press.
 Next generation sequencing data analysis
W. Zheng, L. Chung, H. Zhao (2011) Bias detection and correction in RNA-sequencing data. BMC Bioinformatics, 12: 290.
X. Chen, J. B. Listman, F. Slack, J. Gelernter, H. Zhao (2012) Biases and errors on allele frequency estimation and disease association tests of next generation sequencing of pooled samples. Genetic Epidemiology, 36: 549-560.
J. S. Lee, H. Zhao (2013) On estimation of allele frequencies via next-generation DNA resequencing with barcoding. Statistics in BioSciences, 5: 26-53.
L. M. Chung, J. P. Ferguson, W. Zheng, F. Qian, V. Bruno, R. R. Montgomery, H. Zhao (2013) Differential expression analysis for paired RNA-Seq data. BMC Bioinformatics, 14: 110.
X. Chen, D. Chung, G. Stefani, F. J. Slack, H. Zhao (2015) Statistical issues in binding site identification through CLIP-seq. Statistics and Its Interface, 8: 419–436.
L. Chung, C. Colangelo, H. Zhao (2014) Data pre-processing for label-free multiple reaction monitoring (MRM) experiments. Biology, 3: 383-402.
W. Chen, Y. Cheng, C. Zhang, S. Zhang, H. Zhao (2013) MSClust: A Multi-Seeds based Clustering algorithm for microbiome profiling using 16S rRNA sequence. Journal of Microbiological Methods, 94: 347-355.
W. Chen, C. K. Zhang, Y. Cheng, S. Zhang, H. Zhao (2013) A comparison of methods for clustering 16S rRNA sequences into OTUs. PLoS One, 8: e70837
 Herbal medicine
 Disease biomarker identification