Rebecca Deek

Contributions to Public Health

• My research focuses on statistical and computational methods for omics data, with an emphasis on metagenomics and multiomics integration. As sequencing technologies continue to advance, and their adoption in large-scale medical studies grows, many unique statistical and computational challenges cannot be addressed with existing techniques thus requiring methodological innovation.
• I also work closely with clinicians to analyze genetic, transcriptomic, proteomic, metabolomic, and metagenomic data, as well as their interplay, and determine their impact on human health. Broadly, my research interests are in statistical and machine learning methods for large and complex data sets, motivated by real world applications.
• Statistical and machine learning models for metagenomic data
  • Yan B, Nam Y, Li L, Deek RA, Li H, Ma S. Recent advances in deep learning and language models for studying the microbiome. Frontiers in Genetics. 2024. 15, 1494474.
  • Deek RA, Li H. A zero-inflated latent dirichlet allocation model for microbiome studies. Frontiers in Genetics. 2021. 11:602594.
• Covariation network inference for omics data
  • Deek RA, Li H. Inference of microbial covariation networks using copula models with mixture margins. Bioinformatics. 2023. 39(7):btad413.
• Integrative multiomics analysis
  • Deek RA, Ma S, Lewis JD, Li H. Statistical and computational methods for integrating microbiome, host genomics, and metabolomics data. eLife. 2024. 13:e88956.
  • Duan, H., Ren J, Wei S, Li C, Wang Z, Li M, Wei Z, Yang Z, Liu Y, Xie Y, Wu S, Hu W, Guo C, Zhang X, Liang L, Yu C, Mou Y, Jiang Y, Lu H, Sugarman E, Deek RA, Chen Z, Chen L, Chen Y, Yao M, Liu L, Mou Y, Zhang G. Integrated analyses of Multi-omic data derived from paired primary lung cancer and brain metastasis reveals the metabolic vulnerability as a novel therapeutic target. Genome Medicine. 2024. 16 (1), 1-30.

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