Chang, C., Deng, Y., Jiang, X. and Long, Q.: Multiple Imputation for Analysis of Incomplete Data in Distributed Health Data Networks. Nature Communications 11(1): 5467, 2020.
Chang C, Jang A, Manatunga A, Taylor A.T., Long, Q : A Bayesian Latent Class Model to Predict Kidney Obstruction Based on Renography and Expert Ratings in the Absence of Gold Standard. Journal of the American Statistical Association Page: in press, 2020 Notes: doi.org/10.1080/01621459.2019.1689983.
Bu, Z., Dong, J., Long, Q. and Su, W.: Deep Learning with Gaussian Differential Privacy. Harvard Data Science Review 2020.
Zheng, Q., Dong, J., Long, Q., and Su, W.: Sharp Composition Bounds for Gaussian Differential Privacy via Edgeworth Expansion. The 37th International Conference on Machine Learning (ICML 2020) 2020.
Deng, Y., Jiang, X., and Long, Q.: Privacy-Preserving Methods for Vertically Partitioned Incomplete Data. AMIA'20: AMIA 2020 Annual Symposium 2020.
Min, E.J.*, Safo, S.E., and Long, Q.: Penalized Co-Inertia Analysis with Applications to -Omics Data. Bioinformatics 35(6): 1018-1025, 2018 Notes: *mentee.
Li Z, Roberts K, Jiang X, Long Q: Distributed Learning from Multiple EHR Databases: Contextual Embedding Models for Medical Events. Journal of Biomedical Informatics 92: 103138, 2019 Notes: doi: 10.1016/j.jbi.2019.103138. Epub 2019 Feb 27.
Zhao, Y., Chang, C., and Long, Q.*: Knowledge-guided statistical learning methods for analysis of high-dimensional -omics data in precision oncology. JCO Precision Oncology 3: 1-9, 2019 Notes: *Corresponding author.
Leng Q, Tarbe M, Long Q, Wang F: Pre-existing heterologous T-cell immunity and neoantigen immunogenicity. Clinical & Translational Immunology 9(3): 301111, 2020 Notes: doi: 10.1002/cti2.1111. eCollection 2020. Review.
Safo, S.E.*, Li, S., and Long, Q.: Integrative analysis of transcriptomic and metabolomic data via sparse canonical correlation analysis with incorporation of biological information. Biometrics 74(1): 300-312, 2018 Notes: *mentee.
Chang C, Kundu S, Long Q: Scalable Bayesian variable selection for structured high-dimensional data. Biometrics 74(4): 1372-1382, 2018 Notes: doi: 10.1111/biom.12882. Epub 2018 May 8.
Long, Q., Little, R.J., and Lin, X.: Causal inference in hybrid intervention trials involving treatment choice. Journal of the American Statistical Association 103(482): 474-484, 2008.
Long, Q., Xu, J., Osunkoya, A.O., Sannigrahi, S., Johnson, B.A., Zhou, W., Gillespie, T., Park, J.Y., Nam, R.K., Sugar, L., Stanimirovic, A., Seth, A.K., Petros, J.A., and Moreno, C.S.: Global transcriptome analysis of formalin-fixed prostate cancer specimens identifies biomarkers of disease recurrence. Cancer Research 74(12): 3228-3237, 2014.
Long, Q. and Johnson, B.A.: Variable selection in the presence of missing data: resampling and imputation. Biostatistics 16(3): 596-610, 2015.
Zhao, Y*. and Long, Q.: Multiple imputation in the presence of high-dimensional data. Statistical Methods in Medical Research 25(5): 2021-2035, Oct 2016 Notes: * mentee.
Zhao, Y.*, Chung, M., Johnson, B.A., Moreno, C.S., and Long, Q.: Hierarchical feature selection incorporating known and novel biological information: Identifying genomic features related to prostate cancer recurrence. Journal of the American Statistical Association 111(516): 1427-1439, 2016 Notes: * mentee (An earlier version won Yize Zhao the David P. Byar Travel Award from American Statistical Association’s Biometrics Section 2014).
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Last updated: 11/25/2020
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