SELECTED PUBLICATIONS IN REFEREED STATISTICAL JOURNALS AND CHAPTERS 

• Zhang, R., Zhang, Y, Qu, A., Zhu, Z. and Shen, J. “Covariate-Elaborated Robust Partial Information Transfer with Conditional Spike-and-Slab Prior.” Journal of the American Statistical Association, to appear.
• Rim, J., Xu, Q., Tang, X., Jimah, T., Guo, Y. and Qu, A. (2025) “Heterogeneous effects of physical activity on physiological stress during pregnancy.” PLOS Digital Health, 4(10): e0000837. 
• Yuan, Y., Shahbaba, B., Fortin, N., Cooper, K., Nie, Q. and Qu, A. (2025) “Optimal transport for latent integration with an application to heterogeneous neuronal activity data.” Journal of the American Statistical Association, 120, 1449–1462. (Open Access )
• Wei, K., Xue, F., Xu, Q., Yuan, Y., Zhang, Y, Qin, G., Wani, A., Aiello, A., Wildman, D., Uddin, M. and Qu, A. “Time-varying mediation analysis for incomplete data with application to DNA methylation study for PTSD.” Annals of Applied Statistics, to appear. (Open Access).
• Zhou, R., He, K., Wang, D., Liu, L., Ma, S., Qu, A., Miller, J. P. and Liu, L. (2025) “Neural Network for Correlated Survival Outcomes Using Frailty Model,” Journal of Data Science, 23 (4), 624-637.
• Miao, R., Shahbaba, B. and Qu, A. (2025) “Reinforcement learning for individual optimal policy from heterogeneous data,” Annals of Statistics, 53 (4), 1513-1534. (Open Access) (Selected as 2025 NeurIPs Conference poster by AOS editor).
• Lin, X., Cai, T., Donoho, D., Fu, H., Ke, T., Jin. J., Meng, X.-L., Qu, A., Shi, C., Song, P., Sun, Q., Wang, W., Wu, H., Yu, B., Zhang, H., Zheng, T.*, Zhou, H., Zhou, J., Zhu, H.* and Zhu. J. (2025) “Statistics and AI – A Fireside Conversation.” Harvard Data Science Review, 7(2). https://doi.org/10.1162/99608f92.c066fe9c.
• Rim, J., Xu, Q., Tang, X., Guo, Y. and Qu, A. (2025) “Individualized time-varying nonparametric model with an application in mobile health.” Statistics in Medicine, 44 (5), e70005.
• Ye, H., Zhou, W., Zhu, R. and Qu, A. (2024) “Stage-aware learning for dynamic treatments.” Journal of Machine Learning Research, 25, 1-51. (Henry Ye received 2024 ENAR Van Ryzin best paper award, and 2024 ASA Biometrics Section David Byar Early Career Award, the best paper among all).
• Zhang, C., Xue, L., Chen, Y., Lian, H. and Qu, A. “Local signal detection on irregular domains with generalized varying-coefficient models.” Journal of the American Statistical Association, to appear.
• Zhou, W., Qu, A, Fortin, N., Cooper, K., Shahbaba, B. (2025) “A Model-Agnostic Graph Neural Network for Integrating Local and Global Information.” Journal of the American Statistical Association, 120, 1225-1238.
• Ye, H., Moreno, T., Alpern, A., Ehwerhemuepha, L., Qu, A. (2024)“Dynamic topic language model for heterogeneous children’s mental health clinical notes,” Annals of Applied Statistics, 18, 3165–3184 .
• Zhang, Y., Xu, Q., Tang, N. and Qu, A. (2024) “Differential Private Data Release for Mixed-type Data via Latent Factor Models.” Journal of Machine Learning Research, 25, 1-37.
• Xu, Q., Cao, X., Chen, G., Zeng, H., Fu, H. and Qu, A. (2024) “Multi-label residual weighted learning for individualized combination treatment rule.” Electronic Journal of Statistics, 18,1517-1548.
• Yuan, Y., Xu, Q., Wani, A., Dahrendor, J., Wang, C., Shen, A., Donglasan, J., Burgan, S., Graham, Z., Uddin, M., Wildman, D. and Qu, A. (2024) “Differential expressed heterogeneous dispersion genes testing for count data.” PLOS One, 19(7), e0300565. CRAN R Package
• Liu, L., He, K., Wang, D., Ma, S., Qu, A., Lin L., and Liu, L. (2024) “Health care provider clustering using fusion penalty in quasi likelihood” Biometrical Journal, 66: e202300185.
• Zhang, J., Xue, F., Xu, Q., Lee, J.-A. and Qu, A. (2024) “Individualized Dynamic Model for Multi-resolutional Data with Application to Mobile Health.” Biometrika, 111, 1257-1275. (Jiuchen Zhang received ENAR Student paper award in 2024 and ASA Medical Devices and Diagnostics Student Paper Competition Award in 2024).
• Xu, Q., Fu, H. and Qu, A. (2024) “Optimal Individualized Treatment Rule for Combination Treatments Under Budget Constraints.” Journal of the Royal Statistical Society, Series B, 86, 714-741.
• Zhu, S., Shen, W., Fu, H. and Qu, A. (2024) “Risk-aware Restricted Outcome Learning for Individualized Treatment Regimes.” Annals of Applied Statistics, 18, 1319–1336.
• Yuan, Y. and Qu, A. (2024) “De-confounding causal inference using latent multiple-mediator pathways.” Journal of the American Statistical Association, 119, 2051-2065.
• Liu, L., He, K., Wang, D., Ma, S., Qu, A., Lin, L., Miller, J. P. and Liu, L. (2023) “Healthcare center clustering for Cox’s proportional hazards model by fusion penalty,” Statistics in Medicine, 42, 3685-3698.
• Zheng, J., Wang, J., Xue, L. and Qu, A. (2025) “Semiparametric estimation of non-ignorable missingness with refreshment sample.” Statistica Sinica, 35, 131-150.
• Xu, Q., Yuan, Y., Wang, J. and Qu, A. (2024) “Crowdsourcing utilizing subgroup structure of latent factor modeling.” Journal of the American Statistical Association, 119, 1192-1204. Python Package Coding. (Qi Xu received SLDS student paper award in 2022)
• Li, D., Yuan, Y., Zhang, X. and Qu, A. (2024) “Joint modeling of change-point identification and dependent dynamic community detection.” Statistica Sinica, 34, 1435-1460.
• Zhou, W., Zhu, R. and Qu, A. (2024) “Estimating optimal infinite horizon dynamic treatment regimes via pT-Learning.” Journal of the American Statistical Association, 119, 625–638.
• Zhang, M, Xue, L, Tekwe, C., Bai, Y. and Qu, A. (2023) “Partially functional linear quantile regression with measurement errors.” Statistica Sinica, 33, 2257-2280.
• Deng, Y.*, Yuan, Y.*, Fu, H. and Qu, A. (2023) “Query-augmented active metric learning.” Journal of the American Statistical Association,118, 1862-1875. . (*Joint First Author).
• Yuan, Y. and Qu, A. (2023) “High-order joint embedding for multi-level link prediction.” Journal of the American Statistical Association, 118, 1692-1706.
• Zhang, Y. X., Shi, P., Zhu, Z., Wang, L. and Qu, A. (2023) “Weak signal identification and inference in penalized likelihood models for categorical responses.” Statistica Sinica, 33, 759-786.
• Deng, Y., Tang, X. and Qu, A. (2023) “Correlation tensor decomposition and its application in spatial imaging data.” Journal of the American Statistical Association, 118, 440-456. (Yujia Deng received Imaging Section Student Paper Award in 2019).
• Xue, F., Tang, X., Kim, G., Koenen, K. C., Martin, C. L., Galea, S., Wildman, D., Uddin, M., and Qu, A. (2022) “Heterogeneous Mediation Analysis on Epigenomic PTSD and Traumatic Stress in a Predominantly African American Cohort.” Journal of the American Statistical Association, 117, 1669-1683.
• Zhu, S., Shen, W. and Qu, A. (2022) “Weighted AutoEncoding recommender system.” Statistical Analysis and Data Mining, 15, 570-585.
• Li, Y., Zhu, R., Yeh, M. and Qu, A. (2022) “Dermoscopic image classification with neural style transfer.” Journal of Computational and Graphical Statistics, 31, 1318-1331.
• Xue, F. and Qu, A. (2022) “Semi-standard partial covariance variable selection when irrepresentable conditions fail.” Statistica Sinica, 32, 1881-1909. (Fei Xue received SLDS student paper award in 2018).
• Xue, F., Zhang, Y. Q., Zhou, W., Fu, H. and Qu, A. (2022) “Multicategory angle-based learning for estimating dynamic treatment regimes with censored survival data.” Journal of the American Statistical Association, 117, 1438-1451.
• Bi, X., Adomavicius, G., Li, W. and Qu, A. (2022). “Improving sales forecasting accuracy: A tensor factorization approach with demand awareness.” INFORMS, Journal on Computing, 34, 1644-1660.
• Perera, C., Zhang, H., Zheng, Y., Hou, L., Qu, A., Zheng, C., Xie, K. and Liu, L. (2022). “HIMA2.0: High-dimensional mediation analysis and its application in genome-wide DNA methylation data.” BMC Bioinformatics, 23:296.
• Zhang, J., Yuan, Y. and Qu, A. (2022) “Tensor factorization recommender systems with dependency.” Electronic Journal of Statistics, 16, 2175-2205.
• Xue, F. and Qu, A. (2021) “Integrating multi-source block-wise missing data in model selection.” Journal of the American Statistical Association,  116, 1914-1927. R Package in Cran.
• Li, J. Y., Zhu, R., Qu, A., Ye, H. and Sun, Z. (2021) “Topic modeling on triage notes with semi-orthogonal non-negative matrix factorization” Journal of the American Statistical Association, 116, 1609-1624.
• Fang, F., Zhao, J., Ahmed, S. E. and Qu, A. (2021) “A weak-signal-assisted procedure for variable selection and statistical inference with an informative subsample.” Biometrics, 77, 996-1010.
• Yuan, Y. and Qu, A. (2021) “Community detection with dependent connectivity.” Annals of Statistics, 49, 2378-2428. (Yubai Yuan received SLDS student paper award in 2019.)
• Tang, X., Xue, F. and Qu, A. (2021) “Individualized multi-directional variable selection.” Journal of the American Statistical Association, 116, 1280–1296.
• Dai, B., Shen, X., Wang, J. and Qu, A. (2021)  “Scalable collaborative ranking for personalized prediction.” Journal of the American Statistical Association, 116, 1215–1223.
• Zhang, Y. Q., Bi, X., Tang, N. and Qu, A. (2021).“Dynamic tensor recommender system.” Journal of Machine Learning Research, 22, 1-35.
• Zhu, X., Tang, X. and Qu, A. (2021). “Longitudinal clustering for heterogeneous binary data.” Statistica Sinica, 31, 603-624.
• Bi, X., Tang, X., Yuan, Y., Zhang, Y. and Qu, A. (2021) “Tensor in statistics.” Annual Review of Statistics and Its Application, 8, 345–368.
• Yuan, Y., Deng, Y., Zhang, Y. and Qu, A. (2020). “Deep learning from a statistical perspective.” Stat, 9(1), e294.
• Lyu, Y., Zhu, X., Zhu, Z., and Qu, A. (2020) “Nonparametric cluster analysis on multiple outcomes of longitudinal data.” Statistica Sinica, 30, 1829-1856.
• Wang, Y., Bi, X. and Qu, A. (2020). “A Logistic factorization model for recommender systems with multinomial responses.” Journal of Computational and Graphical Statistics, 29, 396–404.
• Kinson, C., Tang, X., Zuo, Z. and Qu, A. (2020) “Longitudinal principle component analysis with an application on marketing data.” Journal of Computational and Graphical Statistics, 29, 335-350.
• Shen, J. and Qu, A. (2020) “Subgroup analysis based on structured mixed-effects models for longitudinal data.” Journal of Biopharmaceutial Statistics, 30, 607-622.
• Tang, X., Bi, X. and Qu, A. (2020). “Individualized Multilayer Tensor Learning with An Application in Imaging Analysis.” Journal of the American Statistical Association, V. 115, 836-851. (Xiwei Tang received SLDS student paper award in 2017.)
• Zhang, Y. Q., Tang, N. and Qu, A. (2020). “Imputed factor regression for high-dimensional block-wise missing data.” Statistica Sinica, 30, 631-651.
• Xue, L., Shu, X., Shi, P., Wu, C. O. and Qu, A. (2020) “Time-varying feature selection for longitudinal analysis.” Statistics in Medicine, 39, 156-170.
• Xue, L., Shu, X. and Qu, A. (2020)  “Time-varying estimation and dynamic model selection with an application of network data.” Statistica Sinica, 30, 251-284. (Xinxin Shu received Statistical Computing and Graphics student paper award in 2014.)
• Dai, B., Wang, J., Shen, X. and Qu, A. (2019) “Smooth neighborhood recommender systems.” Journal of Machine Learning Research, 20, 1-24.
• Bi, X., Qu, A. and Shen, X. (2018). “Multilayer tensor factorization with applications to recommender systems.” Annals of Statistics, 46, 3308–3333.
• Bi, X. and Qu, A. (2018). “A mixed-effects model for nonignorable missing longitudinal data.” Statistica Sinica, 28, 1653-1675.
• Zheng, X., Xue, L. and Qu, A. (2018). “Time-varying correlation structure estimation and local feature detection for spatio-temporal data.” Journal of Multivariate Analysis, V. 168, 221-239.
• Zhu, X. and Qu, A. (2018). “Cluster analysis of longitudinal profiles with subgroups.” Electronic Journal of Statistics, V. 12, 171-193.
• Wang, J., Shen, X., Sun, Y. and Qu, A. (2017). “Automatic tagging with existing and novel tags.” Biometrika, V. 104, 273-290.
• Bi, X., Qu, A., Wang, J. and Shen, X. (2017). “A group-specific recommender system.” Journal of the American Statistical Association, V. 112, 1344-1353. Matlab coding. (Xuan Bi received SLDS student paper award in 2016.)
• Shi, P. and Qu, A. (2017). “Weak Signal identification and inference in penalized model selection.” Annals of Statistics, V. 45, 1214-1253. (Peibei Shi received SLDS student paper award in 2015 and ENAR student paper award in 2015.)
• Cho, H., Wang, P. and Qu, A. (2017). “Personalized treatment for longitudinal data.” Statistica Sinica, V. 27, 187-205.
• Zhao, P., Tang, N., Qu, A. and Jiang, D. (2017). “Semiparametric estimating equations inference with nonignorable nonresponse.” Statistica Sinica, V. 27, 89-113.
• Shu, X., Wang, J., Shen, X. and Qu, A. (2017). “Words segmentation in Chinese language processing.” Statistics and Its Interface, V. 10, 165-173.
• Zhu, X. and Qu, A. (2016). “Individualizing drug dosage with longitudinal data.” Statistics in Medicine, V. 35, 4474-4488.
• Wang, J., Shen, X., Sun, Y. and Qu, A. (2016). “Classification with unstructured predictors and an application to sentiment analysis.” Journal of the American Statistical Association, V. 111, 1242-1253.
• Wang, P., Zhou, J. and Qu, A. (2016). “Model selection for correlation structure for diverging clustered data.” Canadian Journal of Statistics, V. 44, 343-360.
• Tang, X. and Qu, A. (2016) “Mixture modeling for longitudinal data.” Journal of Computational and Graphical Statistics, V. 25, 1117-1137.
• Cho, H. and Qu, A. (2015). “Consistent moment selection from high-dimensional moment conditions.” Electronic Journal of Statistics, V. 9, 1315-1334.
• Jianhua Hu, Peng Wang  and Annie Qu (2015). “Estimating and identifying unspecified correlation structure for longitudinal data.” Journal of Computational and Graphical Statistics, V. 24, 455–476.
• Xuan Bi and Annie Qu (2015). “Sufficient dimension reduction for correlated data.” Statistica Sinica, V. 25, 787-807.
• Xianlong Wang and Annie Qu (2014). “Efficient classification for longitudinal data.” Computational Statistics and Data analysis, V. 78, 119-134.
• Lily Wang, Lan Xue, Annie Qu and Hua Liang (2014). “Estimation and model selection in generalized additive partial linear models for correlated data with diverging number of covariates.” Annals of Statistics, V. 42, 592-624.
• Hyunkeun Cho and Annie Qu (2013). “Model selection for correlated data with diverging number of parameters.” Statistica Sinica, V. 23, 901-927. PDF file.
• Lan Xue and Annie Qu (2012). “Variable selection in high-dimensional varying-coefficient models with global optimality.” Journal of Machine Learning Research, V. 13, 1973-1998.
• Peng Wang, Guei-feng Tsai, and Annie Qu (2012). “Conditional inference functions for mixed-effects models with unspecified random-effects distribution.” Journal of the American Statistical Association, V. 107, 725-736. PDF file, Appendix.
• Jianhui Zhou and Annie Qu (2012). “Innformative estimation and selection of correlation structure for longitudinal data.” Journal of the American Statistical Association, V. 107, 701-710. PDF file, R coding.
• Lan Wang, Jianhui Zhou and Annie Qu (2012). “Penalized generalized estimating equations for high-dimensional longitudinal data analysis.” Biometrics, V. 68, 353-360.
• Annie Qu, Grace Y. Yi, Peter X.-K. Song and Peng Wang (2011). “Assessing the validity of weighted generalized estimating equations.” Biometrika, V. 98, 215-224.
• Lan Xue, Annie Qu and Jianhui Zhou (2010). “Consistent model selection for marginal generalized additive model for correlated data.” Journal of the American Statistical Association, V. 105, 1518-1530.
• Annie Qu, Bruce G. Lindsay and Lin Lu (2010). “Highly efficient aggregate unbiased estimating functions approach for correlated data with missing at random.” Journal of the American Statistical Association, V. 105,194-204.
• Lan Xue, Li Wang, and Annie Qu (2010). “Incorporating correlation for multivariate failure time data when cluster size is large.”  Biometrics, V. 66, 393-404.
• Peter Song, Zhichang Jiang, Eunjoo Park and Annie Qu (2009). “Quadratic inference functions in marginal models for longitudinal data.” Statistics in Medicine, V. 28, 3683-3696.
• Dziak, J. J., Li, R and Annie Qu (2009) “An overview on quadratic inference function approaches for longitudinal data.” Frontiers of Statistics, Vol 1: New developments in biostatistics and bioinformatics, edited by Fan, J., Liu, J. S. and Lin, X. World scientific publishing, Chapter 3, 49-72.
• Lan Wang and Annie Qu (2009) “Consistent model selection and data-driven tests for longitudinal data in the estimating equation approach.” Journal of the Royal Statistical Society, Series B, V. 71, 177-190 .
• Annie Qu, J. Jack Lee and Bruce G. Lindsay (2008), “Model diagnostic tests for selecting informative correlation structure in correlated data.” Biometrika, V. 95, 891-905.
• Peng Zhang, Peter Song, Annie Qu and Tom Greene (2008), “Efficient estimation of treatment effects on disease progression using non-normal random effects models.”  Biometrics, V. 64, 29-38.
• Guei-Feng Tsai and Annie Qu (2008), “Testing the significance of cell-cycle patterns in time-course microarray data using nonparametric quadratic inference functions.” Computational Statistics & Data Analysis, V. 52, 1387-1398.
• Peter Song, Peng Zhang and Annie Qu (2007), “Maximum likelihood inference in robust linear mixed-effects models using multivariate t distribution.” Statistica Sinica, V. 17, 929-943.
• Lan Wang and Annie Qu (2007), “Robust tests in regression models with omnibus alternatives and bounded influence.” Journal of the American Statistical Association, V. 102, 347-358.
• Annie Qu and Runze Li (2006), “Quadratic inference functions for varying coefficient models with longitudinal data.” Biometrics, V.  62,  379-391.
• Annie Qu and Peter Song (2004), “Assessing robustness of generalised estimating equations and quadratic inference functions.” Biometrika, V. 91, 447-459.
• Bruce Lindsay and Annie Qu (2003), “Inference functions and quadratic score tests.” Statistical Science, V. 18: 394-410.
• Annie Qu and Bruce Lindsay (2003), “Building adaptive estimating equations when inverse of covariance estimation is difficult.” Journal of the Royal Statistical Society, Series B, V. 65: 127-142.
• Annie Qu and Peter Song (2002), “Testing ignorable missingness in estimating equation approaches for longitudinal data.” Biometrika, V. 89: 841-850.
• Annie Qu, Bruce Lindsay and Bing Li (2000), “Improving generalised estimating equations using quadratic inference functions.” Biometrika, V. 87: 823-836.
• Annie Qu and Lan Xue (2009),  Discussion of “Gaining efficiency via weighted estimators for multivariate failure time data” by Fan, Zhou, Cai and Chen. Science in China, Series A: Mathematics, V. 52, 1134-1136.
• Annie Qu (2006),  Book review “Mixed models: Theory and Application” by Eugene Demidenko. Biometrics, V. 62, 304-305.

Collaborative Publications in Refereed Journals: 

• Ju, E.,Qu, A., Guo, Y., Park, J., Kim, J. and Lee, J.-A. (2025) “Sleep Quality of Persons with Dementia and Family Caregivers in Korean Americans: Wearable Technology to Study the Dyadic Association.” Western Journal of Nursing Research, 47 (5), 404-412.
• Ju, E., Burton, C., Kim, J., Guo, Y., Park, J., Qu, A., Gibbs, L. and Lee, J.-A. (2024) “Sleep Disturbances and Interrelationship between Persons with Dementia and Family Caregivers: The Lived Experiences of Korean American Dyads.” Geriatric Nursing, 55, 144-151.
• Guo, Y., Xu, Q., Borg, H., Dutt, N., Kehoe, P, Qu, A. (2023) “Longitudinal Objective Changes in Sleep Structure During Pregnancy.” Women’s Health, 19, 1-8.
• Dahrendorff, J., Wani, A., Keller, T. E., Armstrong, D., Qu, A., Wildman, D. Valero, M. C., Koenen, K., Aiello, A., Uddin, M. (2024) “Analysis of Post-traumatic Stress Disorder Gene Expression Profiles in a Prospective, Community-based Cohort,” Human Biology, 95, 33-47.
• Occean, J. R., Wani H., Donglasan, J., Aiello, A. E., Galea, S., Koenen, K., Qu, A., Wildman, D. E. and Uddin, M. (2022) “DNA methylation of nuclear factor of activated T cells 1 mediates the prospective relation between exposure to distinct traumatic event types and post-traumatic stress disorder.” Psychiatry Research, 114510. https://doi.org/10.1016/j.psychres.2022.114510.
• Wani, A. H., Aiello, A. E., Kim, G. S., Xue, F., Martin, C. L., Ratanatharathorn, A., Qu, A., Koenen, K., Galea, S., Wildman, D. E., Uddin, M. (2021). “The impact of psychopathology, social adversity and stress-relevant DNAm on prospective risk for post-traumatic stress: A machine learning approach.” Journal of Affective Disorders, 282, 894-905.
• Radaideh, M. I., Pigg, C., Kozlowski, T., Deng, Y., Qu, A. (2020). “Neural-based time series forecasting of loss of coolant accidents in nuclear power plants.” Expert Systems With Applications, 160, 113699.
• Katrinli, S., Stevens, J., Wani, A.H., Lori, A., Kilaru, V., van Rooij, S.J.H., Hinrichs, R., Powers, A., Gillespie, C.F., Michopoulos, V., Gautam, A., Jett, M., Hammamieh, R., Yang, R., Wildman, D., Qu, A., Koenen, K., Aiello, A.E., Jovanovic, T., Uddin, M., Ressler, K.J. and Smith, A.K. (2020). “Evaluating the impact of trauma and PTSD on epigenetic prediction of lifespan and neural integrity.” Neuropsychopharmacology. 45(10):1609-1616.
• Rudahindwa, S., Mutesa, L., Rutembesa, E., Mutabaruka, J., Qu, A., Wildman, D. E., Jansen, S and Uddin, M. (2020). “Transgenerational effects of the genocide against the Tutsi in Rwanda: A PTSD symptom domain analysis.” AAS Open Research. 1. 10. 10.12688/aasopenres.12848.2.
• Stelle, L., Schoenheit, T., Brubaker, A., Tang, X., Qu, P., Cradock, K., Sundaram, M., Higham, A. (2018). “Two-year experience of radioactive seed localization versus wire localization for nonpalpable breast lesions at a large community hospital.” Annals of Surgical Oncology, V. 25, 131-136.
• Liechty, J. M., Bi, X. and Qu, A. (2016). “Feasibility and validity of a statistical adjustment to reduce self-report bias of height and weight in wave 1 of the Add health study.” BMC Medical Research Methodology, V. 16, 124-133.
• Jensen, T. W., Ray, T., Wang, J., Li, X., Naritoku, W. Y., Han, B., Bellafiore, F., Bagaria, S. P., Qu, A., Cui, X., Taylor, C. R., Ray, P. S. (2015). “Diagnosis of Basal-like Breast Cancer using a FOXC1-based assay.” Journal of National Cancer Research, V. 107 (8), djv148.
• Green, D. M., Lange, J. M., Qu, A., Peterson, S. M., Kalapurakal, J. A., Stokes, D. C., Grigoriev, Y. A., Takashima, J. R., Norkool, P., Friedman, D. L. and Breslow, N. E. (2013). “Pulmonary disease after treatment for Wilms tumor: A report from the national Wilms tumor long-term follow-up study.”  Pediatric Blood & Cancer, V. 60, 1721-1726.
• R.A. Spotts, M. Serdani, K.M. Wallis, M. Walter, T. Harris-Virgin, K. Spotts, D. Sugar, C.L. Xiao and A. Qu(2009), “At-harvest prediction of gray mold risk in pear fruit in long -term cold storage.” Crop Protection, V. 28, 414-420.
• J. R. Jaeger, K. C. Olson, T. Delcurto and A. Qu (2008), “Case study: Pattern of parturition as effected by time of feeding and prediction of the time of day that parturition will occur in spring-calving beef cows. ” The Professional Animal Scientist, V. 24, 247-253.
• S. Machado, S. Petrie, K. Rhinhart and A. Qu (2007), “Long-term continuous cropping in the Pacific Northwest: Tillage and fertilizer effects on winter wheat, spring wheat, and spring barley production.” Soil & Tillage Research, V. 94, 473-481.
• M. Serdani, R.A. Spotts, J.M. Calabro, J.D. Postman and A. P. Qu (2006), “Evaluation of the USDA national clomal Pyrus germplasm collection for resistance to Podosphaera leucotricha.” HortScience, V. 41, 717-720.
• Peter Heidmann, Susan Tornquist, Annie Qu and Christopher Cebra (2005), “ Laboratory measures of hemostasis and fibrinolysis after intravenous administration of e-aminocaproic acid in clinically normal horses and ponies.” American Journal of Veterinary Research, V. 66: 313-318.
• Ling Zhang, John Selker, Annie Qu and Ajoy Velayudhan (2001), “Numerical estimation of multicomponent adsorption isotherms in preparative chromatography: implications of experimental error.” Journal of Chromatography, A, V. 934, 13-29.
• S.L. Wimberley, M.T. Haug, K.M. Shermock, A. Qu, J.R. Maurer, A.C. Mehta, R.J. Shilz and S.M. Gordon (2001), “Enhanced Cyclosporine-Itraconazole Interaction with Cola in Lung Transplant Recipients.” Clinical Transplantation, V.15: 116-122.
• I.L. Katzan, A.J. Furlan, L.E. Lloyd, J.I. Frank, D.L. Harper, J.A. Hinchey, J. Hammel, A. Qu, C.A. Sila (2000), “Use of Tissue-Type Plasminogen Activator for Acute Ischemic Stroke, the Cleveland Area Experience.” Journal of American Medical Association, V. 283: 1151-1158.