Dr. Ningning Xu


98% of the human genome are corresponding to non-coding area and include many types of elements that regulate the transcription of genes, such as enhancers that boost the gene transcription and silencers that reduce the gene transcription. Note that such activations or suppressions are sometimes too weak to be identified individually, and pooling the elements together can give significant signals. This is also the rationale of super enhancers or enhancer clusters. The focus of my work will be on the cluster inference of these non-coding regulatory elements. In addition, I also work on the development of statistical methods and/or machine learning methods to analyse multi-omics data, such as genomics and transcriptomics.

Curriculum vitae

I received Bachelor’s degree in Applied Mathematics and Master’s degree in Statistics. I started PhD in Leiden University Medical Center (LUMC), where I focused on the statistical methodology development for multiple testing corrections with applications in pathway analysis in genomics and metabolomics. During that time, I visited University of Milano Bicocca in Italy for 3 months to extend my methods to metabolomics data applications. Since June 2021, I started my postdoc in the lab of Dr. Baoxu Pang at the department of Cell and Chemical Biology, LUMC.


  • Closed testing with Globaltest, with application in metabolomics.

    Xu, N., Solari, A., & Goeman, J. J.

    Biometrics (2022). https://doi.org/10.1111/biom.13693

  • Globaltest confidence regions and their application to ridge regression.

    Xu, N., Solari, A., & Goeman, J. J.

    Biometrical Journal (2021), 63(7), 1351-1365.

  • Weighted input to state stability for delay coupled systems on networks.

    Song, H., Xu, N., Qu, Y., & Li, W.

    Applied Mathematical Modelling (2016), 40(13-14), 6234-6242.


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