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URMC / Labs / Tong Tong Wu Lab / Research Projects

 

Research Projects

High-Dimensional Data Analysis

As the availability of new data sets with a large number of variables grows, the challenge of analyzing these (ultra) high‐dimensional data sets has emerged as one of the most important and challenging topics in statistics. Given a large number of predictor variables, how to select a subset from the pool to build parsimonious models and ascertain scientific interpretations has been an important research topic. My research focuses on development of penalization methods under the sparsity assumption. These methods allow automatic selection of the most informative variables by imposing appropriate penalties on certain parameters to the objective function (e.g., L1 norm penalty on regression coefficients in linear regression). In this context, I have developed computational algorithms in high-dimensional settings. Robust and efficient computation is crucial for high-dimensional data due to the problems of non-tractability, non-differentiability, computing capacity and speed in high dimensions. My research on high-dimensional data has been combined with machine learning (e.g., classification and clustering), and traditional biostatistical topics such as survival and longitudinal data analyses.

  1. Yang, L.** and Wu, T.T.$ (2022) Model-Based Clustering of High-Dimensional Longitudinal Data via Regularization. Biometrics, ePub.
  2. Chang, J., Chen, S., Tang, C.Y., and Wu, T.T. (2021) (alphabetic order) High-Dimensional Empirical Likelihood Inference. Biometrika, Volume 108(1), 127-147.
          R code of a toy example: demo_linreg.zip
  3. Wang, D., Wu, T.T., and Zhao, Y. (2019) Penalized Empirical Likelihood for the Sparse Cox Regression Model. Journal of Statistical Planning and Inference, Volume 201, 71-85.
  4. Chang, J., Tang, C.Y.$, and Wu, T.T.$ (2018) A New Scope of Penalized Empirical Likelihood with High-Dimensional Estimating Equations. Annals of Statistics, Volume 46(6B), 3185-3216.
  5. Wu, T.T.$, Li, G., and Tang, C.Y. (2015) Empirical Likelihood for Censored Linear Regression and Variable Selection. Scandinavian Journal of Statistics, Volume 42, No 3, 798--812.
  6. Tang, C.Y. and Wu, T.T.$ (2014) Nested Coordinate Descent Algorithms for Empirical Likelihood. Journal of Statistical Computation and Simulation, Volume 84, No 9, 1917-1930.
  7. Chen, S., Grant, E.**, Wu, T.T., and Bowman, F. D. (2014) Some Recent Statistical Learning Methods for Longitudinal High-dimensional Data. WIREs Computational Statistics, Volume 6, No 1, 10-18.
  8. Gong, H.$, Wu, T.T.$*, and Clarke, E. (2014) (*: corresponding author) Pathway-Gene Identification for Pancreatic Cancer Survival via Doubly Regularized Cox Regression. BMC Systems Biology, Volume 8 (Suppl 1):S3.
  9. Wu, T.T.$ and Wang, S. (2013) Doubly Regularized Cox Regression for High-dimensional Survival Data with Group Structures. Statistics and Its Interface, Volume 6, No 2, 175-186.
  10. Wu, T.T. (2013) Lasso Penalized Semiparametric Regression on High-Dimensional Recurrent Event Data via Coordinate Descent. Journal of Statistical Computation and Simulation, Volume 83, No 6, 1145-1155.
  11. Wu, T.T.$ and He, X. (2012) Coordinate Ascent for Penalized Semiparametric Regression on High-Dimensional Panel Count Data. Computational Statistics and Data Analysis, Volume 56, No 1, 25-33.
  12. Wu, T.T.$ and Lange, K. (2010) Multicategory Vertex Discriminant Analysis for High-Dimensional Data. Annals of Applied Statistics, Volume 4, No 4, 1698-1721.
  13. Wu, T.T.$ and Lange, K. (2010) The MM Alternative to EM. Statistical Science, Volume 25, No 4, 492-505.
  14. Wu, T.T., Chen YF, Hastie T, Sobel E, Lange K. Genomewide Association Analysis by Lasso Penalized Logistic Regression. Bioinformatics. 2009;25:714-721. PMCID: PMC2732298
  15. Wu, T.T., Lange K. Coordinate Descent Algorithms for Lasso Penalized Regression.  Ann Appl Stat. 2008;2:224-244. 

Empirical Likelihood

I recently gained great interest in empirical likelihood. Empirical likelihood, as the nonparametric counterpart of likelihood method, has desirable merits without requiring parametric model assumptions. However, it encounters substantial difficulties when dealing with high-dimensional statistical problems. My research on empirical likelihood tries to tackle these problems.

  1. Chang, J., Chen, S., Tang, C.Y., and Wu, T.T. (2021) (alphabetic order) High-Dimensional Empirical Likelihood Inference. Biometrika, Volume 108(1), 127-147.
          R code of a toy example: demo_linreg.zip
  2. Wang, D., Wu, T.T., and Zhao, Y. (2019) Penalized Empirical Likelihood for the Sparse Cox Regression Model. Journal of Statistical Planning and Inference, Volume 201, 71-85.
  3. Chang, J., Tang, C.Y.$, and Wu, T.T.$ (2018) A New Scope of Penalized Empirical Likelihood with High-Dimensional Estimating Equations. Annals of Statistics, Volume 46(6B), 3185-3216.
  4. Wu, T.T.$, Li, G., and Tang, C.Y. (2015) Empirical Likelihood for Censored Linear Regression and Variable Selection. Scandinavian Journal of Statistics, Volume 42, No 3, 798--812.
  5. Tang, C.Y. and Wu, T.T.$ (2014) Nested Coordinate Descent Algorithms for Empirical Likelihood. Journal of Statistical Computation and Simulation, Volume 84, No 9, 1917-1930.

Longitudinal Data Analysis and Survival Analysis

My Ph.D. training on survival analysis focused on two-stage sampling design and nonparametric/semiparametric modeling. With that background, I have combined my research expertise in high-dimensional data analysis to survival analysis and study the identification of important predictors correlated with patient survival.

  1. Yang, L.** and Wu, T.T.$ (2022) Model-Based Clustering of High-Dimensional Longitudinal Data via Regularization. Biometrics, accepted.
  2. Yang, L.**, Young, D.R.$, and Wu, T.T.$ (2022) Clustering of Longitudinal Physical Activity Trajectories Among Young Females with Selection of Associated Factors. PLOS ONE, 17(5):e0268376.
  3. Leon, S.**, Ren, J.**, Choe, R., and Wu, T.T. (2022) Semiparametric Mixed-Effects Model for Analysis of Non-invasive Longitudinal Hemodynamic Responses During Bone Graft Healing. PLOS ONE, accepted.
  4. LaLonde, A.**, Love, T., Young, D.R., and Wu, T.T. (2022) Clustering Adolescent Female Physical Activity Levels with an Infinite Mixture Model  on Random Effects. Proceedings of the International Federation of Classification Societies (IFCS) 2022, accepted.
  5. Wang, D., Wu, T.T., and Zhao, Y. (2019) Penalized Empirical Likelihood for the Sparse Cox Regression Model. Journal of Statistical Planning and Inference, Volume 201, 71-85.
  6. Fang, H.-B., Wu, T.T.$, Rapoport, A.P., and Tan, M. (2016) Survival Analysis with Functional Covariates for Partial Follow-up Studies. Statistical Methods in Medical Research, Volume 25(6), 2405-2419.
  7. Wu, T.T.$, Li, G., and Tang, C.Y. (2015) Empirical Likelihood for Censored Linear Regression and Variable Selection. Scandinavian Journal of Statistics, Volume 42, No 3, 798--812.
  8. Gong, H.$, Wu, T.T.$*, and Clarke, E. (2014) (*: corresponding author) Pathway-Gene Identification for Pancreatic Cancer Survival via Doubly Regularized Cox Regression. BMC Systems Biology, Volume 8 (Suppl 1):S3.
  9. Chen, S., Grant, E.**, Wu, T.T., and Bowman, F. D. (2014) Some Recent Statistical Learning Methods for Longitudinal High-dimensional Data. WIREs Computational Statistics, Volume 6, No 1, 10-18.
  10. Wu, T.T.$ and Wang, S. (2013) Doubly Regularized Cox Regression for High-dimensional Survival Data with Group Structures. Statistics and Its Interface, Volume 6, No 2, 175-186.
  11. Wu, T.T. (2013) Lasso Penalized Semiparametric Regression on High-Dimensional Recurrent Event Data via Coordinate Descent. Journal of Statistical Computation and Simulation, Volume 83, No 6, 1145-1155.
  12. Wu, T.T.$ and He, X. (2012) Coordinate Ascent for Penalized Semiparametric Regression on High-Dimensional Panel Count Data. Computational Statistics and Data Analysis, Volume 56, No 1, 25-33.
  13. Wu, T.T.$, Gong, H., and Clarke, E. (2011) A Transcriptome Analysis by Lasso Penalized Cox Regression for Pancreatic Cancer Survival. Journal of Bioinformatics and Computational Biology, Volume 9, Suppl. 1, 1-11.
  14. Li G, Wu, T.T. (2010) Semiparametric Additive Risks Regression for Two-Stage Design Survival Studies. Statistica Sinica, Volume 20(4), 1581-1607

Machine Learning

I am also interested in machine learning, in particular, classification and clustering of high-dimensional data.

  1. Yang, L.** and Wu, T.T.$ (2022) Model-Based Clustering of High-Dimensional Longitudinal Data via Regularization. Biometrics, accepted.
  2. Yang, L.**, Young, D.R.$, and Wu, T.T.$ (2022) Clustering of Longitudinal Physical Activity Trajectories Among Young Females with Selection of Associated Factors. PLOS ONE, 17(5):e0268376.
  3. LaLonde, A.**, Love, T., Young, D.R., and Wu, T.T. (2022) Clustering Adolescent Female Physical Activity Levels with an Infinite Mixture Model  on Random Effects. Proceedings of the International Federation of Classification Societies (IFCS) 2022, accepted.
  4. Aditya, P., Sen, R., Oh, S.J., Benenson, R., Bhattacharjee, B., Druschel, P., Wu, T.T., Fritz, M., and Schiele, B. (2016) I-Pic: A Platform for Privacy-Compliant Image Capture. ACM MobiSys 2016 TPC, 235--248.
  5. Wu, T.T.$, Lange K. Matrix Completion Discriminant Analysis. Computational Statistics and Data Analysis 2015;92:115-125.
  6. Wu, T.T.$ and Wu, Y. (2012) Nonlinear Vertex Discriminant Analysis with Reproducing Kernels. Statistical Analysis and Data Mining, Volume 5, No 2, 167-176.
  7. Wu, T.T.$ and Lange, K. (2010) Multicategory Vertex Discriminant Analysis for High-Dimensional Data. Annals of Applied Statistics, Volume 4, No 4, 1698-1721.
  8. Lange, K. and Wu, T.T. (2008) An MM Algorithm for Multicategory Vertex Discriminant Analysis. Journal of Computational and Graphical Statistics, Volume 17, No 3, 527-544.

Computational Genetics

My methodology work on high-dimensional data analysis and survival analysis was motivated and has been applied to genetic and biological studies. Applying my analytic tools designed for high-throughput genetic data, I succeeded in identifying genetic signatures of a small number of relevant predictors of diseases from a pool of thousands of genes or millions of single nucleotide polymorphisms (SNPs). With the determination of genetic signatures, the diagnostic procedure will be greatly simplified for clinical applications.

  1. Costello JC, Heiser LM, Georgii E, Gonen M, Menden MP, Wang NJ, Bansal M, Ammad-ud-din M, Hintsanen P, Khan SA, Mpindi JP, Kallioniemi O, Honkela A, Aittokallio T, Wennerberg K, NCI DREAM Community, Collins JJ, Gallahan D, Singer D, Saez-Roriquez J, Kaski S, Gray JW, Stolovitzky G. (2014) A community effort to assess and improve drug sensitivity prediction algorithms. Nat Biotechnol. 2014;32:1202-1212.  PMCID: PMC4547623
  2. Gong H*, Wu, T.T.*, Clarke E. (*: joint first author and corresponding author) Pathway-Gene Identification for Pancreatic Cancer Survival via Doubly Regularized Cox Regression. BMC Systems Biology. 2014;8 Suppl 1:S3.  PMCID: PMC4080266
  3. Wu, T.T., Gong H, Clarke EM. A Transcriptome Analysis by Lasso Penalized Cox Regression for Pancreatic Cancer Survival. J Bioinform Comput Biol. 2011;9 Suppl 01:63-73. 
  4. Park CC, Ahn S, Bloom JS, Lin A, Wang RT, Wu, T.T., Sekar A, Khan AH, Farr CJ, Lusis AJ, Leahy RM, Lange K, Smith DJ. Fine mapping of regulatory loci for mammalian gene expression using radiation hybrids. Nat Genet. 2008;40:421-429.  PMCID: PMC3014048
  5. Wu, T.T.*, Sun, W.*, Yuan, S., Chen, C.-H., and Li, K.-C. (2008) (*: joint first authors) A Method for Analyzing Censored Survival Phenotype with Gene Expression Data. BMC Bioinformatics, Volume 9, 417.

Epidemiology

I have been working with a team of epidemiologists to study the effects of lifestyle intervention and physical activities on health. For example, in the TAAG studies, we have followed up a group of adolescent girls for over 10 years to investigate multi-level factors affecting their physical activities and sedentary behaviors over time.

  1. Yang, L.**, Young, D.R.$, and Wu, T.T.$ (2022) Clustering of Longitudinal Physical Activity Trajectories Among Young Females with Selection of Associated Factors. PLOS ONE, 17(5):e0268376.
  2. LaLonde, A.**, Love, T., Young, D.R., and Wu, T.T. (2022) Clustering Adolescent Female Physical Activity Levels with an Infinite Mixture Model  on Random Effects. Proceedings of the International Federation of Classification Societies (IFCS) 2022, accepted.
  3. Wu, T.T.#, Xiao, J.,Manning, S.**, Saraithong, P., Pattanaporn, K., Paster, B., Chen, G., Vasani, S., Zeng, Y., Gilbert, C., and Li, Y. (2022) Multimodal data integration reveals mode of delivery and snack consumption outrank salivary microbiome in association with caries outcome in Thai children. Frontiers in Cellular and Infection Microbiology, Volume 12:881899.
  4. Al-Jallad, N.H., Vasani, S., Wu, T.T.#, Cacciato, R., Thomas, M., and Xiao, J. (2022) Racial and oral health disparity associated with Perinatal oral healthcare utilization among underserved US pregnant women. Quintessence International, accepted.
  5. Alzamil, H., Wu, T.T.#, van Wijngaarden, E., Mendoza, M., Malmstrom, H., Fiscella, K., Kopycka-Kedzierawski, D.T., Billings, R.J., and Xiao, J. (2021) Removable Denture Wearing as Risk Predictor for Pneumonia Incidence and Time-to-Event in Older Adult. JDR Clinical & Translational Research, October 2021. doi:10.1177/23800844211049406.
  6. Albelali, A., Wu, T.T.#, Malmstrom, H., and Xiao, J. (2021) Early Childhood Caries Experience Associated with Upper Respiratory Infection in US Children: Findings from a Retrospective Cohort Study. Journal of Pediatrics & Child Health Care, Volume 6(2), 1044.
  7. Koebnick, C., Saksvig, B.I., Li, X., Sidell, M., Wu, T.T.#, and Young, D.R. (2020) The Accuracy of Self-Reported Body Weight Is High but Dependent on Recent Weight Change and Negative Affect in Teenage Girls. International Journal of Environmental Research and Public Health, Volume 17(21), 8203.
  8. Ciminelli, J.T.**, Love, T., and Wu, T.T.$ (2019) Social Network Spatial Model. Spatial Statistics, Volume 29, 129-144.
  9. Xiao, J., Fogarty, C., Wu, T.T.#, Alkhers, N., Zeng, Y., Thomas, M., Youssef, M., Wang, L., Cowens, L., Abdelsalam, H., and Nikitkova, A. (2019) Oral health and Candida carriage in socioeconomically disadvantaged US pregnant women. BMC Pregnancy and Childbirth, Volume 19, Article number: 480.
  10. Young, D.R., Sidell, M., Koebnick, C., Saksvig, B.I., Mohan, Y., Cohen, D., and Wu, T.T.#* (2019) Longitudinal Sedentary Time among Females from Ages 17 to 23 Years. American Journal of Preventive Medicine, Volume 56(4), 540-547.
  11. Young, D.R., Cohen, D., Koebnick, C., Mohan, Y., Saksvig, B.I., Sidell, M., and Wu, T.T.#* (2018) Longitudinal Associations of Physical Activity Among Females from Adolescence to Young Adulthood. Journal of Adolescent Health, Volume 63, 466--473.
  12. Groth, S.W., LaLonde, A.**, Wu, T.T.#, and Fernandez, I.D. (2018) Obesity candidate genes, gestational weight gain and body weight changes in pregnant women. Nutrition, Volume 48, 61-66.
  13. Meng, Y.**, Groth, S.W., Quinn, J., Bisognano, J.D., and Wu, T.T. (2017) An Exploration of Gene-Gene Interactions and Their Effects on Hypertension. International Journal of Genomics, Volume 2017, Article ID 7208318.
  14. Grant EM, Young DR*, Wu, T.T.* (*: joint senior authors) (2015) Predictors for Physical Activity in Adolescent Girls Using Statistical Shrinkage Techniques for Hierarchical Longitudinal Mixed Effects Models. PLoS ONE. 2015;10:e0125431.  PMCID: PMC441601
  15. Zook, K.R., Saksvig, B.I., Wu, T.T.#, and Young, D.R. (2014) Physical Activity Trajectories and Multi-Level Factors among Adolescent Girls. Journal of Adolescent Health, Volume 54, No 1, 74-80.
  16. Young DR, Saksvig BI, Wu, T.T., Zook K, Li X, Champaloux S, Grieser, M, Lee S, Treuth MS. (2014) Multilevel Correlates of Physical Activity For Early, Mid, and Late Adolescent Girls. J Phys Act Health. 2014;11:950-960.
  17. Morrison S, Shenassa E, Mendola P, Wu, T.T., Schoendorf K. (2013) Allostatic load may not be associated with chronic stress in pregnant women, NHANES 1999-2006. Ann Epidemiology. 2013;23:294-297.  PMCID: PMC4132932
  18. Young DR, Camhi S, Wu, T.T., Hagberg J, Stefanick ML. (2013) Relationships Among Changes in C-Reactive Protein and Cardiovascular Disease Risk Factors With Lifestyle Interventions. Nutr Metab Cardiovasc Dis. 2013;23:857-863.  PMCID: PMC3502629

Biomedical Engineering

I have also worked with researchers in the field of bioengineering.

  1. Leon, S.**, Ren, J.**, Choe, R., and Wu, T.T. (2022) Semiparametric Mixed-Effects Model for Analysis of Non-invasive Longitudinal Hemodynamic Responses During Bone Graft Healing. PLOS ONE, accepted.
  2. Johnson, T., Dar, I., Donohue, K., Xu, Y., Santiago, E., Selioutski, O., Marinescu, M., Maddox, R., Wu, T.T., Schifitto, G., Gosev, I.,  Choe, R., and Khan, I. (2022) Cerebral Blood Flow Hemispheric Asymmetry in Comatose Adults Receiving Extracorporeal Membrane Oxygenation. Frontiers in Neuroscience, Volume 16:858404.
  3. Ren, J., Ramirez, G.A., Proctor, A.R., Wu, T.T.#, Benoit, D., and Choe, R. (2020) Spatial Frequency Domain Imaging for Longitudinal Monitoring of Vascularization during Mouse Femoral Graft Healing. Biomedical Optics Express, Volume 11(10), 5442-5455.
  4. Konkel, B., Lavin, C., Wu, T.T.#, Anderson, E., Iwamoto, A., Rashid, H., Gaitian, B., Boones, J., Cooper, M., Abrams, P., Gilbert, A., Tang, Q., Levi, M., Fujimoto, J.G., Andrews, P., and Chen, Y. (2019) Fully Automated Analysis of OCT Imaging of Human Kidneys for Prediction of Post-transplant Function. Biomedical Optics Express, Volume 10(4), 1794-1821.
  5. Wang, B., Wang, H.-W., Guo, H., Anderson, E., Tang, Q., Wu, T.T., Falola, R., Smith, T., Andrews, P.M., and Chen, Y. (2017) Optical Coherence Tomography (OCT) and Computer-Aided Diagnosis (CAD) of a Murine Model of Chronic Kidney Disease (CKD). Journal of Biomedical Optics, Volume 22(12), 1-11.
  6. Tang, Q., Wang, J., Frank, A., Lin, J., Li, Z., Chen, C.W., Jin, L., Wu, T.T., Greenwald, B.D., Mashimo, H., and Chen, Y. (2016) Depth-resolved imaging of colon tumor using optical coherence tomography and fluorescence laminar optical tomography. Biomedical Optics Express, Volume 7, 5218-5232.
  7. Ramirez, G., Proctor, A.R., Jung, K.W., Wu, T.T.#, Han, S., Adams, R.R., Ren, J., Byun, D.K., Madden, K.S., Brown, E.B., Foster, T.H., Farzam, P., Durduran, T., and Choe, R. (2016) Chemotherapeutic drug-specific alteration of microvascular blood flow in murine breast cancer xenografts as measured by diffuse correlation spectroscopy. Biomedical Optics Express, Volume 7, Issue 9, 3610-3630.

HIV/AIDS

I worked on HIV/AIDS data when I was a graduate student at UCLA. I re-gained the interest in HIV/AIDS since I joined CFAR (Center for AIDS Research) at UofR. I welcome collaborations in the related areas.

  1. Jaworski, J.P., Bryk, P., Brower, Z., Zheng, B., Hessell, A.J., Rosenberg, A.F., Wu, T.T., Sanz, I., Keefer, M.C., Haigwood, N.L., and Kobie, J.J. (2017) Pre-Existing Neutralizing Antibody Mitigates B cell Dysregulation and Enhances the Env-Specific Antibody Response  in SHIV-Infected Rhesus Macaques. PLoS One, 12(2):e0172524.
  2. Liu, H., Miller, L.G., Golin, C.E., Hays, R.D., Wu, T.T., Wenger, N.S., and Kaplan, A.H. (2007) Repeated Measures Analyses of Dose Timing of Antiretroviral Medication and its Relationship to HIV Virologic Outcomes. Statistics in Medicine, Volume 26, No 5, 991-1007.
  3. Liu H, Miller LG, Hays RD, Golin CE, Wu, T.T., Wenger NS, Kaplan AH (2006) Repeated Measures Longitudinal Analyses of HIV Virologic Response as A Function of Percent Adherence, Dose Timing, Genotypic Sensitivity, and Other Factors. J Acquir Immune Defic Syndr. 2006;41:315-322.  PMID: 16540932

Medicine and Dentistry

I have also worked with researchers in various health-related fields, such as cancer, pain, dentistry, etc.

  1. Wu, T.T.#, Xiao, J.,Manning, S.**, Saraithong, P., Pattanaporn, K., Paster, B., Chen, G., Vasani, S., Zeng, Y., Gilbert, C., and Li, Y. (2022) Multimodal data integration reveals mode of delivery and snack consumption outrank salivary microbiome in association with caries outcome in Thai children. Frontiers in Cellular and Infection Microbiology, Volume 12:881899.
  2. Al-Jallad, N.H., Vasani, S., Wu, T.T.#, Cacciato, R., Thomas, M., and Xiao, J. (2022) Racial and oral health disparity associated with Perinatal oral healthcare utilization among underserved US pregnant women. Quintessence International, accepted.
  3. Al-Jallad, N., Ly-Mapes, O., Hao, P., Ruan, J., Ramesh, A., Luo, J., Wu, T.T.#, Dye, T., Rashwan, N., Ren, J., Jang, H., Mendez, L., Alomier, N., Bullock, S., Fiscella, K., and Xiao, J. (2022) Artificial intelligence-powered smartphone application, AICaries, improves at-home dental caries screening in children: moderated and unmoderated usability test. PLOS Digital Health, Volume 1(6): e0000046.
  4. Moroni, I., Danti, F.R., Pareyson, D., Pagliano, E., Piscosquito, G., Foscan, M., Marchi, A., Ardissone, A., Genitrini, S., Wu, T.T., Shy. M.E., and Ramchandren. S. (2022) Validation of the Italian version of the Pediatric CMT Quality of Life Outcome Measure. Journal of the Peripheral Nervous System, accepted.
  5. Zeng, Y., Faddak, A., Alomeir, N., Wu, T.T.#, Rustchenko, E., Qing, S., Bao, J., Gilbert, C., and Xiao, J. (2022) Lactobacillus plantarum disrupts S. mutans-C. albicans cross-kingdom biofilms. Frontiers in Cellular and Infection Microbiology, Volume 12:872012.
  6. Alkhars, N., Zeng, Y., Alomeir, N., Jallad, N.A., Wu, T.T.#, Aboelmagd, S., Youssef, M., Jang, H., Fogarty, C., and Xiao, J. (2022) Oral Candida Predicts Streptococcus Mutans Emergence in Underserved US Infants. Journal of Dental Research, Volume 101(1):54-62.
  7. Alzamil, H., Wu, T.T.#, van Wijngaarden, E., Mendoza, M., Malmstrom, H., Fiscella, K., Kopycka-Kedzierawski, D.T., Billings, R.J., and Xiao, J. (2021) Removable Denture Wearing as Risk Predictor for Pneumonia Incidence and Time-to-Event in Older Adult. JDR Clinical & Translational Research, October 2021. doi:10.1177/23800844211049406.
  8. Xiao, J., Meyerowitz, C., Ragusa, P., Funkhouser, K., Lischka, T.R., Chagoya, L.A.M., Jallad, N.A., Wu, T.T., Fiscella, K., Ivie, E., Strange, M., Collins, J., Kopycka-Kedzierawski, D.T., and National Dental Practice-Based Research Network Collaborative Group (2021) Assess an innovative mDentistry eHygiene model amid the COVID-19 pandemic in the National Dental Practice-Based Research Network: Protocol for design, implementation, and usability testing. JMIR Research Protocols, Volume 10(10):e32345.
  9. Xiao, J., Luo, J., Ly-Mapes, O., Wu, T.T., Dye, T., Al Jalla, N., Hao, P., Ruan, J., Bullock, S., and Fiscella, K. (2021) Assess A Smartphone App (AICaries) that uses artificial intelligence to detect dental caries in children and provide interactive oral health education: Protocol for design and usability testing. JMIR Research Protocols, Volume 10(10):e32921.
  10. Jang, H., Al-Jallad, N., Wu, T.T.#, Zeng, Y., Faddak, A., Malmstrom, H., Fiscella, K., and Xiao, J. (2021) Changes in Candida albicans, Streptococcus mutans and oral health conditions following Prenatal Total Oral Rehabilitation among underserved pregnant women. Heliyon, Volume 7(8), e07871.
  11. Albelali, A., Wu, T.T.#, Malmstrom, H., and Xiao, J. (2021) Early Childhood Caries Experience Associated with Upper Respiratory Infection in US Children: Findings from a Retrospective Cohort Study. Journal of Pediatrics & Child Health Care, Volume 6(2), 1044.
  12. Jang, H., Patoine, A., Wu, T.T.#, Castillo, D.A., and Xiao, J. (2021) Oral Microflora and Pregnancy: A Systematic Review and Meta-Analysis. Scientific Report, Volume 11, 16870.
  13. Wu, T.T.$, Xiao, J., Sohn, M., Fiscella, K., Gilbert, C., Grier, A., Gill, A., and Gill, S. (2021) Machine learning approach identified multiplatform factors for caries prediction in child-mother dyads. Frontiers in Cellular and Infection Microbiology, Volume 11:727630.
  14. Chen, W.**, Fitzpatrick, J., Sozio, S.M., Jaar, B.G., Estrella, M.M., Riascos-Bernal, D.F., c, Qiu, Y., Kurland, I.J., Dubin, R.F., Chen, Y., Parekh, R.S., Bushinsky, D.A., and Sibinga, N. (2021) Identification of Novel Biomarkers and Pathways for Coronary Artery Calcification in Nondiabetic Patients on Hemodialysis Using Metabolomic Profiling. Kidney360, Volume 2(2), 279-289.
  15. Ramchandren, S., Wu, T.T.#, Finkel, R., Siskind, C., Feely, S., Burns, J.,  Reilly, M., Estilow, T., Shy, M., Bacon, C., Shy, R., Cornett, K., Muntoni, F., Day, J., Lloyd, T., Sumner, C., Herrmann, D., Kirk, C., Yum, S. (2021) Development and Validation of the Pediatric CMT Quality of Life Outcome Measure. Annals of Neurology, Volume 89(2), 369-379.
  16. Xiao, J., Fogarty, C., Wu, T.T.#, Alkhers, N., Zeng, Y., Thomas, M., Youssef, M., Wang, L., Cowens, L., Abdelsalam, H., and Nikitkova, A. (2019) Oral health and Candida carriage in socioeconomically disadvantaged US pregnant women. BMC Pregnancy and Childbirth, Volume 19, Article number: 480.
  17. Meng, Y., Wu, T.T., Billings, R., Kopycka-Kedzierawski, D.T., and Xiao, J. (2019) Human genes influence the interaction between Streptococcus mutans and host caries susceptibility: a genome-wide association study in children with primary dentition. International Journal of Oral Science, 11(2):19.
  18. Krieger, N.S., Asplin, J., Granja, I., Ramos, F.M., Flotteron, C., Chen, L., Wu, T.T.#, Grynpas, M., and Bushinsky, D.A. (2019) Chlorthalidone Is Superior to Potassium Citrate in Reducing Calcium Phosphate Stones and Increasing Bone Quality in Hypercalciuric Stone-Forming Rats. Journal of the American Society of Nephrology, Volume 30(7), 1163-1173.
  19. Chen, W., Fitzpatrick, J., Monroy-Trujillo, J., Sozio, S., Jaar, B., Estrella, M., Wu, T.T.#, Melamed, M., Parekh, R., and Bushinsky, D.A. (2019) Diabetes Mellitus Modifies the Associations of Serum Magnesium Concentration with Arterial Calcification and Stiffness in Incident Hemodialysis Patients. Kidney International Reports, Volume 4(6), 806-813.
  20. Xiao, J., Alkhers, N., Kopycka-Kedzierawski, D.T., Billings, R., Wu, T.T.#, Castillo, D., Rasubala, L., Malmstrom, H., Ren, Y., and Eliav, E. (2019) Prenatal Oral Health Care and Early Childhood Caries Prevention: A Systematic Review and Meta-analysis. Caries Research, Volume 53(4), 411-421.
  21. Lee, C.Y., Robinson, D.A., Johnson, C.A., Zhang, Y.**, Wong, J., Joshi, D.J., Wu, T.T.#, and Knight, P.A. (2019) A Randomized Controlled Trial of Liposomal Bupivacaine Parasternal Intercostal Block for Sternotomy. Annals of Thoracic Surgery, Volume 107(1), 128-134.
  22. Chen, W.**, Anokhina, V., Miller, B., Dieudonne, G., Abramowitz, M.K., Kashyap, R., Yan, C., Wu, T.T.#, Bentley, K., and Bushinsky, D.A. (2018) Patients with Advanced Chronic Kidney Disease and Vascular Calcification Have a Large Hydrodynamic Radius of Secondary Calciprotein Particles. Nephrology Dialysis Transplantation, Volume 34(6), 992-1000.
  23. Xiao, J., Huang, X., Alkhers, N., Alzamil, H., Alzoubi, S., Wu, T.T.#, Castillo, D., Campbell, F., Harokopakis-Hajishengallis, E., Koo, H. (2018) Candida albicans and Early Childhood Caries: A Systematic Review and Meta-analysis. Caries Research, Volume 52(1-2), 102-112.
  24. Abu-Farsakh, S., Wu, T.T.#, Lalonde, A.**, Sun, J., and Zhou, Z. (2017) High expression of the Claudin-2 in esophageal carcinoma and precancerous lesions is significantly associated with the bile salt receptors VDR and TGR5. BMC Gastroenterology, 17:33.
  25. Pang, C., Lalonde, A.**, Godfrey, T.E., Que, J., Da, J., Sun, J., Wu, T.T.#, and Zhou, Z. (2017) Bile salt receptor TGR5 is highly expressed in esophageal adenocarcinoma and precancerous lesions with significantly worse overall survival and gender differences. Clinical and Experimental Gastroenterology, Volume 10, 29-37.
  26. Choy, B., Lalonde, A.**, Taboada, S., Wu, T.T.#, and Zhou, Z. (2016) MCM4 and MCM7, Potential Novel Proliferation Markers, Significantly Correlated with Ki67, Bmi1, and Cyclin E Expression in Esophageal Adenocarcinoma, Squamous Cell Carcinoma, and Precancerous Lesions. Human Pathology, Volume 57, 126-135.
  27. Huber, A.R., Tan, D., Sun, J., Dean, D., Wu, T.T.#, and Zhou, Z. (2015) High expression of carbonic anhydrase IX is significantly associated with glandular lesions in gastroesophageal junction and with tumorigenesis markers BMI1, MCM4 and MCM7. BMC Gastroenterology, 15:80.
  28. Saligan, L.N., Levy-Clarke, G., Wu, T.T.#, Faia, L.J., Wroblewski, K., Yeh, S., Nussenblatt, R.B., and Sen, H.N. (2010) Quality of Life in Sarcoidosis: Comparing the Impact of Ocular and Non-Ocular Involvement of the Disease. Ophthalmic Epidemiology, Volume 17, No 4, 217-224.
  29. Hamza, M., Wang, X.M., Wu, T.T.#, Jaber, L., Brahim, J.S., Rowan, J.S., and Dionne, R.A. (2010) Nitric Oxide is Negatively Correlated to Pain During Acute Inflammation. Molecular Pain, Volume 6, No 55.