Selected publications

Published and accepted work in statistics, AI, data science, and collaborative biomedical and clinical research.

* Co-corresponding authors.

Yize Zhao · Yale

2026

  1. Intervention on Modifiable Lifestyle and Physiological Factors via Variational Autoencoder Reveals Changes in Functional Connectivity-Mediated Risk for Alzheimer's Disease

    Orlichenko, A., Ding, S., Johns, E., Gu, Z., Tian, X., Li, X. and Zhao, Y.

    Imaging Neuroscience, accepted (2026).

  2. Orthogonal Sparse Subgraph Alignment for Structure-Function Coupling in Brain Networks

    Gao, H., Ham, D., Zhang, Y., Tian, X., Ding, S., Zhao, Y.* and Li, T.*

    Conference on Neural Information Processing Systems (NeurIPS), accepted (2026).

  3. ELPAC: Endpoint-Anchored Latent Progression with Stage-Varying Multimodal Coordination

    Ding, S., Zhang, Y., Tian, X., Guo, Y. and Zhao, Y.

    Conference on Neural Information Processing Systems (NeurIPS), accepted (2026).

  4. A Bayesian deep learning framework for brain dynamic functional connectivity linked with cognition

    Tian, X., Guo, Y., Ding, S., Zhang, Y., Esserman, D. and Zhao, Y.

    Biometrics, accepted (2026).

  5. Latent space-based network analysis for brain-behavior linking in neuroimaging

    Wang, S., Zhang, X., Liu, Y., Xu, W., Tian, X. and Zhao, Y.

    Nature Methods, 23, 225-235.

  6. A Novel Bayesian Framework Uncovering Brain Connectivity-to-Shape Relationship in Preclinical Alzheimer’s Disease

    Ding, S., Johns, E., Orlichenko, A., Fredericks, C. and Zhao, Y.

    Annals of Applied Statistics, 20(2), 1429–1451 (2026).

  7. Brain Functional-Structural Gradient Coupling Reflects Development, Behavior and Genetic Influences

    Gao, S., Gu, Z., Ding, S., Wang, G., Zhang, Z., Zhao, H. and Zhao, Y.

    Nature Communications, 17, 4850 (2026).

  8. Disentangling Latent Risk Pathways via Bayesian Hypergraph Inference

    Ding, S., Gao, H., Liu, P., Tian, X. and Zhao, Y.

    International Conference on Machine Learning (ICML), Oral, 2026.

  9. Is the whole more than the sum of its parts? Considering global and local features of the connectome improves prediction of individuals and phenotypes

    Riley, S., Cheng, A., Wang, Y., Shen, X., Dhamala, E., Zhao, Y., Holmes, A., Constable, R. and Yip, S.

    Imaging Neuroscience, 4, imag.a.1287 (2026).

2025

  1. A genetically informed brain atlas for enhancing brain imaging genomics

    Bao, J., Wen, J., Chang, C., Mu, S., Chen, J., Shivakumar, M., Cui, Y., Erus, G., Yang, Z., Yang, S., Wen, Z., Zhao, Y., Kim, D., Duong-Tran. D., Saykin, AJ., Zhao, B., Davatzikos, C., Long, Q., Shen, L.

    Nature Communications, 16, 3524.

  2. Bayesian longitudinal network regression with application to brain connectome genetics

    Li, C., Tian, X., Gao, S., Wang, S., Wang, G., Zhao, Y. and Zhao, Y.

    Statistics in Medicine, 44, 8-9, e70069.

  3. Semiparametric Joint Modeling for Biomarker Trajectory Before Disease Onset

    Sun, Y., Zhao, X., Chan, K., Xu, W., Allore, H. and Zhao, Y.

    Biometrics, 81(2), ujaf064.

  4. scMODAL: A general deep learning framework for comprehensive single-cell multi-omics data alignment with feature links

    Wang, G., Zhao, J., Lin, Y., Liu, T., Zhao, Y. and Zhao, H.

    Nature Communications, 16, 4994 (2025).

  5. Cost efficiency of fMRI studies using resting-state vs task-based functional connectivity

    Zhang, X., Hulvershorn, L., Constable, T., Zhao, Y. and Wang, S.

    Human brain mapping, in press.

  6. Characterizing Amyloid Pathogenic Spread in Alzheimer’s Disease Through A Network Diffusion Model

    Xu, F., Duong-Tran, D., Huang, H., Saykin, A., Thompson, P., Davatzikos, C., Zhao, Y. and Shen L.

    ACM-BCB’25, in press.

  7. Supervised brain node and network construction under voxel-level functional imaging

    Xu, W., Wang, S., Tan, C., Shen, X., Luo, W., Constable, T., Li, T. and Zhao, Y.

    Imaging Neuroscience, 3: IMAG.a.56.

  8. Covariance-on-Covariance Regression

    Zhao, Y. and Zhao, Y.

    Biometrics, 81(3), ujaf097.

  9. GenCPM: A Toolbox for Generalized Connectome-based Predictive Modeling

    Xu, B., Ding S., Xu, W., Fredericks, C. and Zhao, Y.

    Frontiers in Neuroscience, in press.

  10. Integrating Slow Neural Oscillations and Physiological Burden for Trait Anxiety Prediction

    Min, J., Chen, J., Bao, J., Yang, S., Zhao, Y., Shen, L., Duong-Tran, D.

    NeurIPS Workshop: Learning from Time Series for Health, 2025.

  11. H-VIP: Quantifying regional topological contributions from the brain network towards cognition

    Garai, S., Vo. S., Blank. L., Xu. F., Chen. J., Duong-Tran. DA., Zhao, Y., Brown, BC., Shen. L., for the ADNI.

    Frontiers in Radiology, in press.

2024

  1. Bayesian semi-parametric inference for clustered recurrent events with zero inflation and a terminal event

    Tian, X., Ciarleglio, M., Cai, J., Greene, E., Esserman, D., Li, F. and Zhao, Y.

    Journal of the Royal Statistical Society: Series C, 73(3), 598–620.

    An earlier version won a student paper award in Bayesian Statistical Science Section of ASA.

  2. Homological landscape of human brain functional sub-circuits

    Duong-Tran, D., Kaufmann, R., Chen, J., Wang, X., Garai, S., …, Zhao, Y., Shen L.

    Mathematics, 12(3), 455.

  3. Topology-based clustering of functional brain networks in an Alzheimer’s disease cohort

    Xu, F., Gao, M,, Chen, J., Garai, S., Duong-Tran, D., Zhao, Y. and Shen, L.

    AMIA-IS’24, 449-458.

  4. Knowledge-guided learning methods for integrative analysis of multi-omics data

    Li, W., Ballard, J., Zhao, Y. and Long, Q.

    Computational and Structural Biotechnology Journal, 23, 1945-1950.

  5. Bayesian mixed model inference for genetic association under related samples with brain network phenotype

    Tian, X., Wang, Y., Wang, S., Zhao, Y., and Zhao, Y.

    Biostatistics, 25(4), 1195-1209.

  6. Caudal and thalamic segregation in white matter brain network communities in Alzheimer's disease population

    Xu, F., Duong-Tran, D., Zhao, Y. and Shen, L.

    IEEE-BHI’24.

  7. Learning High-Order Relationships of Brain Regions

    Qiu, W., Chu, H., Wang, S., Zuo, H., Li, X., Zhao, Y., and Ying, R.

    Proceedings of the International Conference on Machine Learning (ICML).

  8. Heterogeneity analysis on multi-state brain functional connectivity and adolescent neurocognition

    Wang, S., Constable, T., Zhang, H. and Zhao, Y.

    Journal of the American Statistical Association, 119(546), 851-863.

    An earlier version won the Best Student Paper Award at Statistical Methods in Imaging conference.

  9. Establishing group-level brain structural connectivity incorporating anatomical knowledge under latent space modeling

    Wang, S., Wang, Y., Xu, F., Shen, L. and Zhao, Y.

    Medical Imaging Analysis, 103309.

  10. Joint modeling of human cortical structure: genetic correlation network and composite-trait genetic correlation

    Shen, J., Zhang, Y., Zhu, Z., Cheng, Y., Cai, B., Zhao, Y. and Zhao, H.

    NeuroImage, 297, 120739.

  11. Medial Amygdalar Tau Is Associated With Mood Symptoms in Preclinical Alzheimer’s Disease

    Li, J., Tun, S., Ficek-Tani, B., Xu, W., Wang, S., Horien, C., Toyonaga, T., Nuli, S., Zeiss, S., Powers, A., Zhao, Y., Mormino, E. and Fredericks, C.

    Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 9(12), 1301-1311.

  12. A principled framework to assess information theoretical fitness of brain functional sub-circuits

    Duong-Tran, D., Nguyen, N., Mu S., Chen, J., Bao, J., Frederick, X., Garai, S., …, Zhao, Y., Shen L. and Goni., J.

    Mathematics, 12(19), 2967.

  13. Volume-optimal persistence homological scaffolds of hemodynamic networks covary with MEG theta-alpha aperiodic dynamics

    Nguyen, N., Hou, T., Amico, E., Zheng, J., Huang, H., Kaplan, A., Petri, G., Goni, J., Kaufmann, R., Zhao, Y., Shen, L. and Duong-Tran D.

    MICCAI’24.

  14. Multiscale estimation of morphometricity for revealing neuoranatomical basis of cognitive traits

    Wen, Z., Bao, J., Yang, S., Wen, J., Zhan, Q., Cui, Y., Erus, G., Yang, Z., Thompson, P., Zhao, Y., Davatzikos, C. and Shen. L.

    ISBI’24.

  15. Self-reported hearing loss is associated with faster cognitive and functional decline but not diagnostic conversion in the ADNI Cohort

    Miller, A., Sharp, E., Wang, S., Zhao, Y., Mecca, A., van Dyck, C. and O'Dell, R.

    Alzheimer's & Dementia, 20(11), 7847-7858.

  16. Sex-specific topological structure associated with dementia via latent space estimation

    Wang, S., Wang, Y., Xu, F., Tian, X., Fredericks, C., Shen, L. and Zhao, Y.

    Alzheimer's & Dementia, 20(12), 8387-8401.

  17. Collaborative Survival Analysis on Predicting Alzheimer's Disease Progression

    Xu, W., Wang, S., Shen, L. and Zhao, Y.

    Statistics in Biosciences, 1-24.

  18. Heritability and Genetic Contribution Analysis of Structural-Functional Coupling in Human Brain,

    Dai, W., Zhang, Z., Song, P., Zhang, H. and Zhao, Y.

    Imaging Neuroscience, 80(2), 1-9.

  19. Bayesian pathway analysis over brain network mediators for survival data

    Tian, X., Li, F., Shen, L., Esserman, D. and Zhao, Y.

    Biometrics, 80(4), ujae132.

    An earlier version won the ENAR student paper award; and the John Van Ryzin Award.

  20. Antiseizure medications in post-stroke seizures: A systematic review and network meta-analysis

    Misra, S., Wang, S., …, Zhao, Y., Jette, N., Kasner, S., Kwan, P. and Mishra, N.

    Neurology, 104(3), e210231.

  21. Bayesian subtyping for multi-state brain functional connectome with application on adolescent brain cognition

    Chen, T., Tan, C., Zhao, H., Constable, T., Yip, S. and Zhao, Y.

    Biostatistics, 26(1), kxae045.

  22. Bayesian thresholded modeling for integrating brain node and network predictors

    Sun, Z., Xu, W., Li, T., Kang, J., Alanis-Lobato G. and Zhao, Y.

    Biostatistics, 26(1), kxae048.

2023

  1. Bayesian interaction selection model for multi-modal neuroimaging data analysis

    Zhao, Y., Wu, B., Kang, J.

    Biometrics, 79(2), 655-668.

  2. Genetic underpinnings of brain structural connectome for young adults

    Zhao, Y., Chang, C., Zhang, J. and Zhang, Z.

    Journal of the American Statistical Association, 118 (543), 1473-1487.

    An earlier version won the YSPH Investigator Research Award

  3. Dynamic Prediction of Outcomes for Youth at Clinical High Risk for Psychosis: A Joint Modeling Approach

    Worthington, M., Addington, J., Bearden, C, Cadenhead, K., Cornblatt, B., Keshavan, M., Mathalon, D., Perkins, D., Waler, E., Woods, S., Zhao, Y. and Cannon T.

    JAMA Psychiatry, 80(10), 1017-1025.

  4. Impact of Genetic polymorphisms on the risk of epilepsy amongst patients with acute brain injury: a systematic review

    Misra, S., Quinn, T.J., Falcone, G.J., Sharma, V.K., de Havenon, A., Zhao, Y., Eldem, E., French, J.A., Yasuda, C.L., Dawson, J. and Liebeskind, D.S.

    European Journal of Neurology, in press.

  5. Mining correlation between fluid intelligence and whole-brain large scale structural connectivity

    Garai, S., Xu, F., Duong-Tran, D., Zhao, Y. and Shen L.

    AMIA Informatics Summit, 225 233.

  6. Identifying shared neuroanatomic architecture between cognitive traits through multiscale morphometric correlation analysis

    Wen, Z., Bao, J., Yang, S., Risacher, SL., Saykin, AJ., Thompson, PM., Davatzikos, C., Huang, H., Zhao, Y. and Shen L.

    MMMI’23, in press.

  7. Outcomes in patients with poststroke seizures: a systematic Review and meta-analysis

    Misra, S., Kasner, S., Dawson, J., Tomotaka, T., Zhao, Y., Zaveri, H., Eldem E., Vazquez J. Mohidat S… and Mishra, N.

    JAMA Neurology, 80(11), 1155-1165.

  8. Individual Patient Data Meta-Analysis from International Post-Stroke Epilepsy Research Repository (IPSERR) to characterize post-stroke epilepsy population and their Outcomes: A study protocol

    Mishra, N., Kwan, P., Tanaka, T., Zhao, Y., Misra, S….,Kasner, S.

    BMJ Open, in press.

2022

  1. Bayesian sparse heritability analysis with high-dimensional neuroimaging phenotypes

    Zhao, Y., Li, T., Zhu, H.

    Biostatistics, 23(2), 467-484.

  2. Pursuing sources of heterogeneity in modeling clustered population

    Li, Y., Yu, C., Zhao, Y., Aseltine, R., Yao, W., Chen, K.

    Biometrics, 78(2), 716-729.

  3. Identifying highly heritable brain amyloid phenotypes through mining Alzheimer’s imaging and sequencing biobank data

    Bao, J., Wen, Z., Kim, M., Zhao, X., Lee, B., Jung, S., Davatzikos, C., Saykin, A., Thompson, P., Kim, D., Zhao, Y. and Shen, L.

    Pac Symp Biocomput, 27:109-20.

  4. Identifying imaging genetic associations via regional morphometricity estimation

    Bao, J., Wen, Z., Kim, M., Saykin, A., Thompson, P., Zhao, Y. and Shen, L.

    Pac Symp Biocomput, 27:97-108.

  5. Discussion of “Bayesian Graphical Models for Modern Biological Applications'”

    Zhao, Y., Sun, Z., Kang, J.

    Statistical Methods & Applications, 31(2), 279-286.

  6. Using machine learning to predict heavy drinking during outpatient alcohol treatment

    Roberts, W., Zhao, Y., Verplaetse, T., Moore, K., Peltier, M., Burke, C., Zakiniaeiz, Y. and McKee, S.

    Alcoholism: Clinical and Experimental Research, 46(4), 657-666.

  7. A comparison of analytical strategies for cluster randomized trials with survival outcomes and competing risks

    Li, F, Lu, W, Wang, Y., Pan, Z, Greene, E., Meng, G., Meng, C. Blala, O, Zhao, Y., Peduzzi, P. and Esserman, D.

    Statistical Methods in Medical Research, 31(7), 1224-1241.

  8. Sex- specific genetic association between psychiatric disorders and cognition, behavior and brain imaging in children and adults

    Gui Y, Zhou X, Wang Z, Zhang Y, Wang Z, Zhou G, Zhao, Y., Liu M, Lu H, Zhao H.

    Translational Psychiatry, 12: 347

  9. Consistency of graph theoretical measurements of Alzheimer’s disease fiber density connectomes across multiple parcellation scales

    Xu, F., Garai, S., Duong-Tran, D., Saykin, AJ., Zhao, Y. and Shen L, for the ADNI.

    BIBM22, 1323-1328.

  10. Bayesian network mediation analysis with application to the brain functional connectome

    Zhao, Y., Chen, T., Cai, J., Lichenstein, S, Potenza, M. and Yip, S.

    Statistics in Medicine, 41(20), 3991-4005.

2021

  1. Bayesian network-driven clustering analysis with feature selection for high-dimensional multi-modal molecular data

    Zhao, Y., Chang, C., Hannum, M., Lee, J., Shen, R.

    Scientific Reports, 11(1).

  2. A Novel Bayesian Semi-parametric Model for Learning Heritable Imaging Traits

    Zhao, Y., Zhao, X., Bao, J., Min, E. and Shen, Li.

    MICCA’21, 678-687.

2020

  1. Decreased sphingolipid synthesis in children with 17q21 asthma-risk genotypes

    One, J., Kim, B., Zhao, Y., Christos, P., Tesfaigzi, Y., Worgall, T., Worgall, S.

    The Journal of Clinical Investigation, 130(2): 921-926.

  2. Polygenic mediation analysis of Alzheimer's disease implicated intermediate amyloid imaging phenotypes

    Eng, Y., Yao, X., Liu, K., Risacher, S., Saykin, A., Long, Q., Zhao, Y., Shen, L.

    AMIA'20: AMIA 2020 Annual Symposium proceedings, 422-431.

  3. Grading meningiomas utilizing multiparametric MRI with inclusion ofsusceptibility weighted imaging and quantitative susceptibility mapping

    Zhang, S., Chiang, G., Knapp J., Zecca, C., He, D., Ramakrishna, R., Magge, R., Posapia D., Fine, H., Tsiouris, A., Zhao, Y., Heier, L., Wang, Y., Kovanlikaya, I.

    Journal of Neuroradiology, 47:272-277.

2019

  1. Data-Driven Subtyping of Parkinson’s Disease Using Longitudinal Clinical Records: A Cohort Study

    Zhang X., Chou J., Liang J., Xiao C., Zhao, Y., Sarva H., Henchclife C., Wang F.

    Scientific Reports 9:797.

  2. Knowledge-guided statistical learning methods for analysis of high-dimensional -omics data in precision oncology

    Zhao, Y., Chang, C., Long, Q.

    JCO Precision Oncology.

  3. Structured genome-wide association studies with Bayesian hierarchical variable selection

    Zhao, Y., Zhu, H., Lu, Z., Knickmeyer, R., Zou, F.

    Genetics, 212 (2): 397-415.

2018

  1. Comparison of MRI segmentation techniques for measuring liver cyst volumes in autosomal dominant polycystic kidney disease

    Farooq, Z., Behzadi, A., Blumenfeld, J., Zhao, Y., Prince, M.

    Clinical Imaging, 47: 41-46.

  2. Dentate nucleus signal intensity decrease on T1-weighted MR images after switching from gadopentetate dimeglumine to gadobutrol

    Behzadi, A., Farooq, Z., Zhao, Y., Shih, G., Prince, M.

    Radiology, 287(3): 816-823.

  3. Immediate reactions to gadolinium based contrast agents: a systematic review and meta-Analysis

    Behzadi, A., Zhao, Y., Farooq, Z., Prince, M.

    Radiology, 286(2): 471-482.

  4. Diagnostic accuracy of semiautomatic lesion detection plus quantitative susceptibility mapping in the identification of new and enhancing multiple sclerosis lesions

    Zhang, S., Nguyen, T., Zhao, Y., Gauthier, S., Wang, Y.

    NeuroImage: Clinical, 28(18): 143-148.

  5. Regional expression of genes mediating transynaptic alpha-synuclein transfer predicts regional atrophy in Parkinson disease

    Freeze, B., Acosta, D., Pandya, S., Zhao, Y., Raj, A.

    NeuroImage: Clinical, 28: 456-466.

  6. Fast and robust unsupervised identification of MS lesion change using the statistical detection of changes algorithm

    Nguyen, T.D., Zhang, S., Gupta, A., Zhao, Y., Gauthier, S.A., Wang, Y.

    American Journal of Neuroradiology, 39(5): 830-833.

  7. Trifunctional PSMA-targeting constructs for prostate cancer with unprecedented localization to LNCaP tumors

    Kelly, J., Amor-Coarasa, A., Ponnala, S., Nikolopoulou, A., Williams, C., Schlyer, D., Zhao, Y., Kim, D., Babich, J.

    European Journal of Nuclear Medicine and Molecular Imaging, 45(11):1841-1851.

  8. Regional vulnerability in Alzheimer's disease: the role of cell-autonomous and transneuronal processes

    Acosta, D., Powell, F., Zhao, Y., Raj, A.

    Alzheimer's & Dementia, 14: 797-810.

  9. Prognostic implications of gadolinium enhancement of skull base chordomas

    Lin, E., Scognamiglio, T., Zhao, Y., Schwartz, T.H., Phillips C.D.

    American Journal of Neuroradiology, 39(8):1509-1514.

  10. Reliability and agreement of sodium (23NA) MRI in calf muscle and skin of healthy subjects from the US

    Dyke, J.P., Meyring-Wosten, A., Zhao, Y., Linz, P., Thijssen, S., Kotanko, P.

    Clinical Imaging, 52, 100-105.

  11. Bayesian spatial variable selection for ultra-high dimensional neuroimaging data: a multiresolution approach

    Zhao, Y., Kang, J., Long, Q.

    IEEE/ACM Transactions on Computational Biology and Bioinformatics, 15(2):537-550.

  12. Magnetic resonance neurography of the lumbosacral plexus for lower extremity radiculopathy: frequency of findings, characteristics of abnormal intraneural signal, and correlation with electromyography

    Chazen, J.L., Cornman-Homono, J., Zhao, Y., Sein, M., Feuer, N.

    American Journal of Neuroradiology, 39 (11): 2154-2160.

  13. Relationship of seminal megavesicles, prostate median cysts, and genotype in autosomal dominant polycystic kidney disease

    Zhang, W., Stephens, C., Blumenfeld, J., Behzadi, A., Donohue, S., Bobb, W., Newhouse, J., R ennert, H., Zhao, Y., Prince, M.

    Journal of Magnetic Resonance Imaging, 49 (3): 894-903.

  14. Knowledge-guided Bayesian support vector machine for high-dimensional data with application to genomic Data

    Sun, W., Chang, C., Zhao, Y., and Long, Q.

    IEEE International Conference on Big Data (IEEE BigData 2018), 1484-1493.

2017

  1. ``Bayesian feature selection for ultra-high dimensional imaging genetics data''. In:

    Zhao, Y., Zou, F., Lu, Z., Knickmeyer, R., Zhu, H.

    Imaging Genetics, Ed. by A. Dalca, et al.. Elsevier Science.

  2. The use of noncontrast quantitative MRI to detect gadolinium-enhancing multiple sclerosis brain lesions: a systematic review and meta-analysis

    Gupta, A., Al-Dasuqi, K., Xia, F., Askin, G., Zhao, Y., Delgodo, D., Wang, Y.

    American Journal of Neuroradiology, 38(7): 1317-1322.

  3. Variable selection in the presence of missing data: imputation-based methods

    Zhao, Y., Long, Q.

    WIREs Computational Statistics, 9:e1402.

  4. Complex liver cysts in autosomal dominant polycystic kidney disease

    Farooq, Z., Behzadi, A., Zhao, Y., Prince, M.

    Clinical Imaging, 46: 98-101.

  5. Imputation with High-dimensional Data

    Zhao, Y., Long, Q.

    Wiley StatsRef-Statistics Reference Online, stat08004.

2016

  1. Modeling clinical outcome using multiple correlated functional biomarkers: a Bayesian approach

    Long, Q., Zhang, X., Zhao, Y., Johnson, B.A., Bostick, R.M.

    Statistical Methods in Medical Research, 25(2): 520-537.

  2. Multiple imputation in the presence of high-dimensional data

    Zhao, Y., Long, Q.

    Statistical Methods in Medical Research, 25(5): 2021-2035.

  3. Bayesian Network Feature Finder (BANFF): an R package for gene network feature selection

    Lan, Z., Zhao, Y., Kang, J., Yu, T.

    Bioinformatics, 32(23): 3685-3687.

  4. Hierarchical feature selection incorporating known and novel biological information: identifying genomic features related to prostate cancer recurrence

    Zhao, Y., Chung, M., Johnson, B.A., Moreno, C., Long, Q.

    Journal of the American Statistical Association, 111(516): 1427-1439.

    An earlier version won a student paper award in Biometrics Section at the Joint Statistical Meetings.

2014

  1. Very high-dose cholecalciferol and arteriovenous stula maturation in ESRD patients: a randomized, double-blind, placebo-controlled pilot study

    Wasse, H., Huang, R., Long, Q., Zhao, Y., Singapuri, S., Tangpricha, V.

    Journal of Vascular Access, 15 (2): 88-94.

  2. A Bayesian nonparametric mixture model for selecting genes and gene sub-networks

    Zhao, Y., Kang, J., Yu, T.

    Annals of Applied Statistics, 8(2): 999-1021.

2013

  1. Assessing association between p-value list

    Yu, T., Zhao, Y., Shen, S.

    Statistical Analysis and Data Mining, 6 (2): 144-155.

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