Hongyu Zhao, PhD

Department Chair and Ira V. Hiscock Professor of Biostatistics, Professor of Genetics and Professor of Statistics and Data Science

Research Interests

Genetics; Public Health; Computational Biology; Statistics; Genomics; Herbal Medicine; Proteomics; Biostatistics; Microbiota

Research Organizations

School of Public Health: YSPH Global Health Concentration

Cancer Genetics

Center for Medical Informatics

Keck: High Performance Computation | NIDA Neuroproteomics Center

Obesity Research Working Group

Yale Cancer Center: Genomics, Genetics, and Epigenetics

Yale Institute for Global Health

Research Summary

Our research is driven by the need to analyze and interpret large and complex data sets in biomedical research. For example, in genome wide association studies involving thousands to hundreds of thousands of individuals, millions of DNA variants are analyzed for each person. Such data offer people the opportunity to identify variants affecting disease susceptibility and develop risk prediction models to facilitate disease prevention and treatment. There are many statistical challenges arising from the analysis of such data, including the very high dimensionality, the relatively weak signals, and the need to incorporate prior knowledge and other data sets in analysis. Another example is the analysis of next generation sequence data which present even greater statistical and computational challenges. Our group has been developing statistical methods to address these challenges, such as empirical Bayes methods to borrow information across different data sets, different generalizations of Gaussian graphical models for network inference, Markov random field models for spatial and temporal modeling, and general machine learning methods for high dimensional data.

Specialized Terms: Statistical genomics and proteomics; Bioinformatics; Data integration; High dimensional data; Network and graphical models; Disease risk prediction; Herbal medicine; Microbiome; Cancer genomics

Extensive Research Description

  • Genome Wide Associatio Studies: We are developing statistical methods to integrate diverse data types and prior biological knowledge to identify genes for common diseases and risk prediction models. We also develop methods to infer the genetic architecture of complex diseases and for risk predictions.
  • Network Modeling: We are developing statistical methods to model biological networks under the general framework of Gaussian and other graphical models. Specific networks we are working on include gene expression regulatory networks, signaling networks, and eQTL networks.
  • Cancer Genomics: We are developing statistical and computational methods to analyze cancer genomics data, e.g. microarrays and next generation sequencing, to identify cancer subtypes, driver mutations, and appropriate treatments for cancer patients.
  • Microbiome: We are developing modeling and analysis approaches for microbiome data generated from next generation sequencing data.
  • Proteomics: Our current focus is on targeted proteomics, such as Multiple Reaction Monitoring.
  • Herbal Medicine: Through systems biology approach, we are identifying tissue-specific target pathways of herbal medicine.

Selected Publications

Full List of PubMed Publications

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