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Research

Selected publications:

[1] 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.

[2] 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.

[3] 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.

[4] 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.

[5] 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.

[6] 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.

[7] 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.

[8] Proteomics

L. Chung, C. Colangelo, H. Zhao (2014) Data pre-processing for label-free multiple reaction monitoring (MRM) experiments. Biology, 3: 383-402.

[9] Microbiomes

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

[10] Herbal medicine

[11] Disease biomarker identification