My research is focused on bioinformatics. I develop methods in the areas of metabolomics, exposomics, and systems biology. In my collaborative research, I work on virology/vaccinology, nutrition, and cancer. I am affiliated with the Emory Vaccine Center.
My personal webpage is at http://web1.sph.emory.edu/users/tyu8/
Areas of Interest
- Cancer Prevention
- HIV/AIDS Prevention
- Environmental Health
Affiliations & Activities
Associated Associate Professor, Department of Mathematics and Computer Science.
Affiliate faculty member, Emory Vaccine Center.
- Yu T, Park Y, Li S, Jones DP, 2013, Hybrid feature detection and information accumulation using high-resolution LC-MS metabolomics data., J. Proteome Res., 12, 1419-1427
- Zhao Y*, Kang J*#, Yu T#, 2014, A Bayesian nonparametric mixture model for selecting genes and gene sub-networks, Ann. Appl. Stat., 8, 999-1021
- Yang R, Bai Y, Qin ZS, Yu T, 2014, EgoNet: identification of human disease ego-network modules., BMC Genomics, 15, 314
- Yu T, Jones DP., 2014, Improving peak detection in high-resolution LC/MS metabolomics data using preexisting knowledge and machine learning approach., Bioinformatics, 30, 2941-2948
- Chong E, Huang Y, Wu H, Ghasemzadeh N, Uppal K, Quyyumi AA, Jones DP, Yu T, 2015, Local false discovery rate estimation using feature reliability in LC/MS metabolomics data., Scientific Reports, 5, 17221
- Yan Y*, Qiu S*, Jin Z, Gong S, Bai Y#, Lu J#, Yu T#, 2017, Detecting subnetwork-level dynamic correlations., Bioinformatics, 33, 256-265
- Liao P, Wu H#, Yu T#, 2017, ROC Curve Analysis in the Presence of Imperfect Reference Standards., Statistics in Biosciences, 9, 91-104
- Cai Q, Kang J#,Yu T# , 2017, Network Marker Selection for Untargeted LC/MS Metabolomics Data. , J. Proteome Res., 16, 1261-1269.
- Jin Z, Kang J#,Yu T# , 2018, Missing value imputation for LC-MS metabolomics data by incorporating metabolic network and adduct ion relations., Bioinformatics, 34, 1555-1561
- Yu T, 2018, A new dynamic correlation algorithm reveals novel functional aspects in single cell and bulk RNA-seq Data, PLoS Computational Biology, 14, e1006391
- Kong Y, Yu T, 2018, A graph-embedded deep feedforward network for disease outcome classification and feature selection using gene expression data., Bioinformatics, Epub ahead of print,
- Fei T, Zhang T, Shi W#, Yu T#, 2018, Mitigating the adverse impact of batch effects in sample pattern detection., Bioinformatics, Epub ahead of print,