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postdoctoral scholar

UC Davis Genome Center - Metabolomics
GBSF building
451 East Health Science Drive




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Research interests
Development of analytical technologies to enhance metabolomic coverage and improve the confidence of metabolite identification
Integration of metabolomics with other ‘omics’ (epigenomics, genomics, transcriptomics, and proteomics) data for the systems-level interrogation of biological problems
Application of state-of-art metabolomics technologies in biomarker discovery
2019-2023: Ph.D. in Chemistry, The University of British Columbia, Canada
2014-2018: B.Sc. in Chemistry, Zhejiang University, China
Yu, H., Low, B., Zhang, Z., Guo, J., Huan, T., Quantitative Challenges and Their Bioinformatic Solutions in Mass Spectrometry-Based Metabolomics. Trends in Analytical Chemistry, 2023
Yu, H., Huan, T. MAFFIN: Metabolomics Sample Normalization Using Maximal Density Fold Change with High-Quality Metabolic Features and Corrected Signal Intensities. Bioinformatics, 2022
Yu, H., Sang, P., Huan, T. Adaptive Box-Cox Transformation: A Highly Flexible FeatureSpecific Data Transformation to Improve Metabolomics Data Normality for Better Statistical Analysis. Analytical Chemistry, 2022
Yu, H., Huan, T. Comprehensive Assessment of the Diminished Statistical Power Caused by Nonlinear Electrospray Ionization Responses in Mass Spectrometry-Based Metabolomics. Analytica Chimica Acta, 2022.
Yu, H., Huan, T. Patterned Signal Ratio Biases in Mass Spectrometry-Based Quantitative Metabolomics. Analytical Chemistry, 2021.
Yu, H., Chen, Y., Huan, T. Computational Variation: An Under-Investigated Quantitative Variability Caused by Automated Data Processing in Untargeted Metabolomics. Analytical Chemistry, 2021.
Yu, H., Xing, S., Nierves, L., Lange, P. F., & Huan, T. Fold-Change compression: An unexplored but correctable quantitative bias caused by nonlinear electrospray ionization responses in untargeted metabolomics. Analytical Chemistry, 2020.
Yu, H., Villanueva, N., Bittar, T., Arsenault, E., Labonté, B., & Huan, T. Parallel metabolomics and lipidomics enables the comprehensive study of mouse brain regional metabolite and lipid patterns. Analytica Chimica Acta, 2020.