Biography

I am an Assistant Professor in the Department of Population and Data Sciences, the Department of Pediatrics, and a member of the Quantitative Biomedical Research Center (QBRC) and the Harold C. Simmons Cancer Center at UT Southwestern Medical Center. My primary statistical expertise is in machine learning, algorithm development for genomics, transcriptomics, epigenomics, and single-cell sequencing data, and development of data management system. During my Ph.D. career in the Biostatistics programs in Cornell University, I received comprehensive training in integrated analysis of high-dimensional datasets, machine learning, Bayesian modeling and developing bioinformatics algorithms to analyze next-generation sequencing data. With training as a postdoctoral fellow, I have developed machine learning tools and sequencing data analysis algorithms for identifying predictive and prognostic biomarkers, as well as potential therapeutic targets for personalized medicine.

Since I joined UT Southwestern as an Assistant Professor, my lab has developed a series of bioinformatics algorithms and deep learning models to identify new disease genes and therapeutic targets (e.g. Xu et al. Cell Reports (2019), Huang et al. Nature (2020), Lu et al. Nature Machine Intelligence (2022), Xu et al. Nature Communications (2022), Chai et al. Nature Medicine (2022), Takahiko et al. Science Translational Medicine (2022) (Cover Story), Shi et al. Cancer Cell (2022), Lebek et al. Science (2023)). Since 2019, my lab’s research works have been published in a series of high-impact journals, including Nature, Science, Nature Medicine, Cancer Cell, Elife, PNAS, Nature Machine Intelligence, Nature Communications, Nature Metabolism, Science Translational Medicine, Genes & Development, Cell Reports, Journal of Clinical Investigation, Cancer Research, Circulation Research, and Developmental Cell.

With extensive experience in data science, I have been the leading PI or co-PI of multiple grants from a variety of funding resources, including the National Cancer Institute (NCI), National Heart, Lung, and Blood Institute (NHLBI), National Human Genome Research Institute (NHGRI), National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS), Cancer Prevention and Research Institute of Texas (CPRIT), Hyundai Foundation, Children’s Cancer Fund, Rally Foundation, and Andrew McDonough B+ Foundation.

Research Interest

  • machine learning, deep learning, and algorithm development for multi-omics data

Publications

Featured Publications LegendFeatured Publications

Development of a Data Model and Data Commons for Germ Cell Tumors.
Ci B, Yang DM, Krailo M, Xia C, Yao B, Luo D, Zhou Q, Xiao G, Xu L, Skapek SX, Murray MM, Amatruda JF, Klosterkemper L, Shaikh F, Faure-Conter C, Fresneau B, Volchenboum SL, Stoneham S, Lopes LF, Nicholson J, Frazier AL, Xie Y, JCO Clin Cancer Inform 2020 Jun 4 555-566
Tumor neoantigenicity assessment with CSiN score incorporates clonality and immunogenicity to predict immunotherapy outcomes.
Lu T, Wang S, Xu L, Zhou Q, Singla N, Gao J, Manna S, Pop L, Xie Z, Chen M, Luke JJ, Brugarolas J, Hannan R, Wang T, Sci Immunol 2020 Feb 5 44

Honors & Awards

  • Independent Investigator Research Award from Rally Foundation
    (2020)
  • Career Development Award from Children’s Cancer Fund
    (2019)

Professional Associations/Affiliations

  • Department of Pediatrics (2019)
  • Quantitative Biomedical Research Center, Department of Population and Data Sciences, Peter O’Donnell Jr. School of Public Health (2019)