UT Southwestern Medical Center


I am an Associate Professor in the Department of Population and Data Sciences, and a member of the Quantitative Biomedical Research Center and the Harold C. Simmons Cancer Center at UT Southwestern Medical Center. I have extensive experience in data integration, predictive modeling, spatial modeling and image analysis. I am the PI of an NCI RO1 grant focused on developing novel statistical methods for integrating different types of genomics data for new drug discovery. I was also the PI on an NIH R21/R33 grant for the epigenetic study of psychological diseases, and an NSF grant on developing statistical models to study spatial patterns of genomic data. My methodology research is focused on model development for high-dimensional data such as gene expression, epigenomics, copy number variation, proteomics, genome-wide RNAi functional screening data and brain image data. Since 2014, my lab starts to work on developing methodology for pathology image analysis. We have collected a large amount of high-quality digital pathological data and genomic data in lung cancer and developed pathological image analysis pipelines for lung cancer, kidney cancer and head and neck cancer.

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Research Summary

I am interested in developing computational models, algorithms for big data to predict patients' conditions which can help clinicians to tailor treatment plans for individual patients. My researches have develped statistical methodologies for Bayesian analysis, new bioinformatics tools, and gene expression signatures to predict patient prognosis and response to chemotherapy. Besides my research in developing statistical methodology, I am also devoted to apply the state-of-art statistical methods in biomedical research.

Research Interests

  • Computation methods in digital pathology
  • Spatial statistics and modeling
  • Statistical modeling in biomedical research
  • Integrative analysis of high-dimensional data
  • Prediction Model and biomarker discovery
  • Bayesian Modeling

Methodology development for tumor pathology image and brain image analysis

my lab is actively developing statistical methods and analysis pipelines for brain imaging data, such as functional magnetic resonance imaging (fMRI).

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Develop statistical methods for high-dimensional data and spatial modeling

We are developing statistical methodologies for Bayesian analysis, spatial modeling and integrative analysis of different molecular profiling datasets.

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Develop computational algorithms and bioinformatics tools

We are actively developing new bioinformatics tools and computational algorithms for big data, such as genome-wide RNAi screening data and next-generation sequencing data.

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Selected Publications

Based on an analysis of two large databases on breast cancer, reduced activity of an autophagy gene, beclin 1, was related to both a higher incidence of triple-negative breast cancer and a poorer prognosis for breast cancer patients.

Genomic regression analysis of coordinated expression.

Nature Communications 8, Article number: 2187 (2017) doi:10.1038/s41467-017-02181-0
Cai L, Li Q, Du Y, Yun J, Xie Y, DeBerardinis RJ, Xiao G.

publisher's website

Using functional signature ontology (FUSION) to identify mechanisms of action for natural products.

Science Signal, Vol. 6, Issue 297, pp. ra90
Potts MB, Kim HS, Fisher KW, Hu Y, Carrasco YP, Bulut GB, Ou YH, Herrera-Herrera ML, Cubillos F, Mendiratta S, Xiao G, Hofree M, Ideker T, Xie Y, Huang LJ, Lewis RE, MacMillan JB, White MA.

publisher's website

Akt-mediated regulation of autophagy and tumorigenesis through Beclin 1 phosphorylation.

Science, Vol. 338, Issue 6109, pp. 956-959 DOI: 10.1126/science.1225967
Wang RC, Wei Y, An Z, Zou Z, Xiao G, Bhagat G, White M, Reichelt J, Levine B.

publisher's website

Try Out Our Software

We have developed online analysis tools which allows users to explore and analyze lung cancer, germ cell tumor relative gene expresion data. PIPECLIP Galaxy is also provided for biologists to identify the most likely cross-linking sites.