Yuqing Zhang
Ph.D. Candidate, Bioinformatics, BUSM
Johnson Lab
Education
B.S. Applied Mathematics, Peking University, Beijing, China, 2015
M.S. Bioinformatics, Boston University, Boston MA, 2017
Contact Information
Email: yuqingz@bu.edu
Research Interests
Statistical models for batch effect diagnosis and adjustment. I develop methods to detect batch effect in genomic data, and design novel algorithms to address batch effect for various data types and situations.
Data heterogeneity in statistical learning with genomic data. Heterogeneity in genomic data is caused by complicated biological and technical factors. I generate realistic simulations in attempt to understand how these factors affect the validation of prediction models, and how to reduce the impact of heterogeneity on prediction.
Publications
Zhang, Y., Bernau, C., Parmigiani, G., & Waldron, L. (2018). The impact of different sources of heterogeneity on loss of accuracy from genomic prediction models. Biostatistics (Oxford, England).
Zhang, Y., Jenkins, D. F., Manimaran, S., & Johnson, W. E. (2018). Alternative empirical Bayes models for adjusting for batch effects in genomic studies. BMC bioinformatics, 19(1), 262.
Griffin, P. J., Zhang, Y., Johnson, W. E., & Kolaczyk, E. D. (2018). Detection of multiple perturbations in multi‐omics biological networks. Biometrics, 74(4), 1351-1361.