Dr Na You
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Email
na.you@essex.ac.uk -
Location
3A.536, Colchester Campus
Profile
Biography
Na You is a Senior Lecturer in Statistics at the Department of Mathematical Sciences of the University of Essex. Prior to joining the University of Essex in 2023, Dr. You was a Professor of Statistics at Sun Yat-sen University (Guangzhou) where she started as an Associate and worked for 12 years since 2011. Before that, she obtained her PhD degree from Peking University in 2005, and had a 5-year postdoc training at University of California, Riverside, USA. Dr. You is interested in the methodology research in Biostatistics and its applications in medical research, especially for (ultra-)high dimensional data analysis. Her research topics cover multiple testing, variable selection, mixture model and non-parametric statistics, for the identification of genomic biomarkers from (ultra-)high dimensional data, and the prediction of patient's prognosis. Recently, she focus on nonparametric testing, regression with non-Euclidean predictors, and their applications for treatment effect evaluation and image data analysis.
Qualifications
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PhD Peking University, (2005)
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BSc Yantai University, (2000)
Appointments
University of Essex
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Senior Lecturer, Department of Mathematical Sciences, University of Essex (22/5/2023 - present)
Other academic
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Professor, School of Mathematics, Sun Yat-sen University (10/4/2018 - 30/6/2023)
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Visiting Scholar, School of Medicine, Johns Hopkins University (1/8/2013 - 31/7/2014)
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Associate Professor, School of Mathematics, Sun Yat-sen University (28/6/2011 - 10/4/2018)
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Postdocotoral Researcher, Department of Statistics, University of California, Riverside (1/10/2006 - 10/6/2011)
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Postdoctoral Researcher, School of Public Health, Peking University (1/7/2005 - 5/6/2007)
Research and professional activities
Current research
Survival association analysis and feature screening in general metric spaces
Existing dependence measures are primarily designed for either Euclidean data or fully observed outcomes, which limits their applicability to survival association studies involving imaging objects. To address this gap, I am working on a novel dependence measure that quantifies the association between random objects in a metric space and right-censored survival outcomes, along with a hypothesis testing procedure for assessing their independence. High-resolution medical images often contain thousands of candidate regions, although only a small subset is truly associated with clinical outcomes. Identifying survival-relevant imaging regions is essential for understanding the dependence structure between imaging objects and survival outcomes. To this end, I am developing an independence screening procedure that identifies survival-relevant regions while controlling false discoveries in high-dimensional settings.
Conditional independence testing and causal network construction
While association analysis identifies relevant imaging features, understanding the underlying mechanisms of disease progression requires moving beyond association toward causal inference. I am aiming to extend the dependence measure for random objects in a metric space and survival outcomes to conditional settings, enabling conditional independence testing between imaging features and survival outcomes while adjusting for demographic, clinical, and imaging covariates. The conditional independence testing can then be integrated with graphical modelling approaches to construct a causal network that elucidates the mechanisms through which imaging biomarkers influence disease progression.
Imaging biomarker identification in Breast Cancer progression
Deep learning models applied to raw medical images are susceptible to domain shift, which can substantially degrade their performance across datasets. In contrast, shape-based imaging representations are often more robust to batch effects and variations in imaging protocols. I am collaborating with clinicians to integrate multiple breast cancer imaging datasets for large-scale cohort analysis, with the aim of identifying novel imaging biomarkers.
Integrative prognostic modelling using multimodal data
The development of prognostic signatures is a well-established area of clinical research, and such signatures are widely used for patient stratification and treatment decision-making. With the increasing adoption of medical imaging in clinical practice, integrating imaging biomarkers into prognostic signatures offers significant potential to improve risk prediction. By combining multimodal biomarkers and accounting for their underlying dependence and causal structures, it is possible to develop prognostic signatures that are more accurate, robust, and interpretable.
Teaching and supervision
Current teaching responsibilities
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Data analysis and statistics with R (MA334)
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Modelling experimental and observational data (MA335)
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Dissertation (MA981)
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Mathematics of Portfolios (MA311)
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Financial Mathematics (MA226)
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Commutative Algebra (MA316)
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Ordinary Differential Equations (MA202)
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Analytical Mechanics (MA222)
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Financial Derivatives (MA320)
Publications
Journal articles (27)
You, N., Dai, H., Wang, X. and Yu, Q., (2024). Sequential estimation for mixture of regression models for heterogeneous population. Computational Statistics and Data Analysis. 194, 107942-107942
You, N., Xueyi, H., Dai, H. and Xueqin, W., (2023). Ball divergence for the equality test of crossing survival curves. Statistics in Medicine. 42 (29), 5353-5368
Chen, B., He, X., Pan, B., Zou, X. and You, N., (2021). Comparison of beta diversity measures in clustering the high-dimensional microbial data. PloS one. 16 (2), e0246893-e0246893
Chen, B., You, N., Pan, B., He, X. and Zou, X., (2021). Application of Clustering Method to Explore the Correlation Between Dominant Flora and the Autism Spectrum Disorder Clinical Phenotype in Chinese Children. Frontiers in Neuroscience. 15, 760779-
Yang, W., You, N., Jia, M., Yeung, S-CJ., Ou, W., Yu, M., Wang, Y., Fu, X., Zhang, Z., Yang, J., Lao, Z., Liu, Z., Zeng, B., Ou, Q., Wu, X., Shao, YW., Hong, X., Wang, S. and Cheng, C., (2020). Undetectable circulating tumor DNA levels correlate with low risk of recurrence/metastasis in postoperative pathologic stage I lung adenocarcinoma patients. Lung Cancer. 146, 327-334
Tang, J., Zhang, C-B., Lyu, K-S., Jin, Z-M., Guan, S-X., You, N., Huang, M., Wang, X-D. and Gao, X., (2020). Association of polymorphisms in C1orf106, IL1RN, and IL10 with post-induction infliximab trough level in Crohn’s disease patients. Gastroenterology Report. 8 (5), 367-373
Zhong, W., Tan, L., Jiang, WG., Chen, K., You, N., Sanders, AJ., Liang, G., Liu, Z., Ling, Y. and Gong, C., (2019). Effect of younger age on survival outcomes in T1N0M0 breast cancer: A propensity score matching analysis. Journal of Surgical Oncology. 119 (8), 1039-1046
You, N., He, S., Wang, X., Zhu, J. and Zhang, H., (2018). Subtype Classification and Heterogeneous Prognosis Model Construction in Precision Medicine. Biometrics. 74 (3), 814-822
You, N. and Wang, X., (2017). An empirical Bayes method for robust variance estimation in detecting DEGs using microarray data. Journal of Bioinformatics and Computational Biology. 15 (05), 1750020-1750020
Wu, W., Deng, H., Rao, N., You, N., Yang, Y., Cao, M. and Liu, J., (2017). Neoadjuvant everolimus plus letrozole versus fluorouracil, epirubicin and cyclophosphamide for ER-positive, HER2-negative breast cancer: study protocol for a randomized pilot trial. Trials. 18 (1), 497-
Zhao, J., Wang, Y., Lao, Z., Liang, S., Hou, J., Yu, Y., Yao, H., You, N. and Chen, K., (2017). Prognostic immune-related gene models for breast cancer: a pooled analysis. OncoTargets and Therapy. Volume 10, 4423-4433
Liang, H., Xiang, Y-Q., Lv, X., Xie, C-Q., Cao, S-M., Wang, L., Qian, C-N., Yang, J., Ye, Y-F., Gan, F., Ke, L-R., Yu, Y-H., Liu, G-Y., Qiu, W-Z., Huang, X-J., Wen, C-H., You, N., Wang, X-Q., Guo, X. and Xia, W-X., (2017). Survival impact of waiting time for radical radiotherapy in nasopharyngeal carcinoma: A large institution-based cohort study from an endemic area. European Journal of Cancer. 73, 48-60
Wen, C-H., Ou, S-M., Guo, X-B., Liu, C-F., Shen, Y-B., You, N., Cai, W-H., Shen, W-J., Wang, X-Q. and Tan, H-Z., (2017). B-CAN: a resource sharing platform to improve the operation, visualization and integrated analysis of TCGA breast cancer data. Oncotarget. 8 (65), 108778-108785
Miller, ER., Cooper, LA., Carson, KA., Wang, N-Y., Appel, LJ., Gayles, D., Charleston, J., White, K., You, N., Weng, Y., Martin-Daniels, M., Bates-Hopkins, B., Robb, I., Franz, WK., Brown, EL., Halbert, JP., Albert, MC., Dalcin, AT. and Yeh, H-C., (2016). A Dietary Intervention in Urban African Americans. American Journal of Preventive Medicine. 50 (1), 87-95
Huang, G., Wang, S., Wang, X. and You, N., (2016). An empirical Bayes method for genotyping and SNP detection using multi-sample next-generation sequencing data. Bioinformatics. 32 (21), 3240-3245
Gong, C., Tan, W., Chen, K., You, N., Zhu, S., Liang, G., Xie, X., Li, Q., Zeng, Y., Ouyang, N., Li, Z., Zeng, M., Zhuang, S., Lau, W-Y., Liu, Q., Yin, D., Wang, X., Su, F. and Song, E., (2016). Prognostic Value of a BCSC-associated MicroRNA Signature in Hormone Receptor-Positive HER2-Negative Breast Cancer. EBioMedicine. 11, 199-209
Murillo, GH., You, N., Su, X., Cui, W., Reilly, MP., Li, M., Ning, K. and Cui, X., (2016). MultiGeMS: detection of SNVs from multiple samples using model selection on high-throughput sequencing data. Bioinformatics. 32 (10), 1486-1492
Ding, W., Kou, Q., Wang, X., Xu, Q. and You, N., (2015). Single-sample SNP detection by empirical Bayes method using next-generation sequencing data. Statistics and Its Interface. 8 (4), 457-462
You, N., Mou, P., Qiu, T., Kou, Q., Zhu, H., Chen, Y. and Wang, X., (2012). Gene Expression Network Reconstruction by LEP Method Using Microarray Data. The Scientific World Journal. 2012, 1-6
You, N., Murillo, G., Su, X., Zeng, X., Xu, J., Ning, K., Zhang, S., Zhu, J. and Cui, X., (2012). SNP calling using genotype model selection on high-throughput sequencing data. Bioinformatics. 28 (5), 643-650
Cui, X., You, N., Girke, T., Michelmore, R. and Van Deynze, A., (2010). Single feature polymorphism detection using recombinant inbred line microarray expression data. Bioinformatics. 26 (16), 1983-1989
Xuan Mao, C. and You, N., (2009). On Comparison of Mixture Models for Closed Population Capture–Recapture Studies. Biometrics. 65 (2), 547-553
You, N. and Mao, CX., (2009). On hierarchical loglinear models in capture–recapture studies. Computational Statistics & Data Analysis. 53 (12), 3916-3920
You, N., Liu, J. and Mao, CX., (2008). An Empirical Bayesian Method for Detecting Differentially Expressed Genes Using EST Data. International Journal of Plant Genomics. 2008, 1-4
You, N. and Xuan Mao, C., (2008). Population Size Estimation in a Two‐List Surveillance System with a Discrete Covariate. Biometrics. 64 (2), 371-376
Xu, Y., Liu, L., You, N., Pan, H. and Yip, P., (2007). Estimating Population Size for a Continuous Time Frailty Model with Covariates in a Capture–Recapture Study. Biometrics. 63 (3), 917-921
ZHANG, L., LIU, L. and YOU, NA., (2005). Estimating Population Size in Logistic Capture-Recapture Models with a Known Sex Ratio. Communications in Statistics - Theory and Methods. 34 (1), 37-44
Book chapters (1)
Mao, CX. and You, N., (2010). Estimate the initial population size from removal data. In: Biometrics Methods Applications and Analysis. 187- 195