Research Interests

Adavanced Data Analytics: machine Learning, statisical modelling and predictive anlaytics, with application to data enriched fields
Techniques: deep learning, deep embedding, graph convolutional networks, empirical Bayes, graphical models, mixed models, mixture models, survival analysis, kernel methods, genetic algorithms
Applications: computational genomics, statistical genetics, text mining (biomedical literature, EHR, analyst reports), digital marketing, web analytics, finance (risk assessement, credit evaluation), microbiome

Selected Papers (Google Scholar)

  • Wei Z and Jensen T Shane, GAME: Detecting Cis-regulatory Elements Using a Genetic Algorithm, Bioinformatics, 2006 22:1577-1584, Software GAME.
  • Wei Z, and Li Mingyao, Genome-wide linkage and association analysis of rheumatoid arthritis in a Canadian population, BMC Proceedings, 1 (Suppl 1), S19 .
  • Wei Z and Li Hongzhe, Nonparametric Pathway-Based Regression Models for Analysis of Genomic Data, Biostatistics, 2007 8: 265-284
  • Wei Z and Li Hongzhe, A Markov Random Field Model for Network-based Analysis of Genomic Data, Bioinformatics, 2007 23:1537-1544
  • Wei Z and Li Hongzhe, A Hidden Spatial-temporal Markov Random Field Model for Network-based Analysis of Time Course Gene Expression Data, Annals of Applied Statistics, 2008, 2: 408-429
  • Wei Z, Li Mingyao, Rebeck T and Li Hongzhe, U-Statistics-based Tests for Multiple Genes in Genetic Association Studies, Annals of Human Genetics, 2008, 72: 821-833
  • Alexander Braunstein, Wei Z, Shane T. Jensen, and Jon D. McAuliffe, A Spatially Varying Two-Sample Recombinant Coalescent, with Applications to Hiv Escape Response, Proceeding of the 22nd annual conference on Neural Information Processing Systems (NIPS), 21(1):193-200, Dec. 8 -13, 2008, Vancouver, B.C., Canada.
  • Wei Z, Sun Wenguang, Wang K and Hakonarson H, Multiple Testing in Genome-Wide Association Studies via Hidden Markov Models, Bioinformatics, 2009 25:2802-2808, Software PLIS. 
  • Wei Z et al, From Association to Disease Risk Prediction: an Optimistic View from Genome-wide Association Studies on Type 1 Diabetes, PLoS Genetics, 2009, 5(10): e1000678
  • Li C, Wei Z, and Li Hongzhe, Network-based Empirical Bayes Methods for Linear Models with Applications to Genomic Data, Journal of Biopharmaceutical Statistics, 2010 20 (2): 209-222.
  • Wang W, Wei Z, and Sun Wenguang, Simultaneous Set-Wise Testing Under Dependence, with Applications to Genome-Wide Association Studies, Statistics and Its Interface, 2010 3 (4): 501-512.
  • Li Hongzhe, Wei Z, and Maris John, A Hidden Markov Random Field Model for Genome-wide Association Studies, Biostatistics, 2010, 11:139-150.
  • Sun Wenguang and Wei Z, Multiple Testing for Pattern Identification, with Applications to Microarray Time Course Experiments, Journal of the American Statistical Association, 2011 106 (493): 73–88, .
  • Roshan U, Chikkagoudar S, Wei Z, Wang K, Hakonarson H, Ranking causal variants and associated regions in genome-wide association studies by the support vector machine and random forest, Nucleic acids research, 39 (9), e62.
  • Wei Z, Wang Wei, Hu P, Lyon GJ, and Hakonarson H, SNVer: a statistical tool for variant calling in analysis of pooled or individual next-generation sequencing data, Nucleic Acids Research,  2011 39 (19): e132. Software SNVer
  • W Wang, Wei Z, TW Lam, and J Wang, Next generation sequencing has lower sequence coverage and poorer SNP-detection capability in the regulatory regions, Scientific Reports, 1:55
  • Daye Z John, Li Hongzhe and Wei Z, A powerful test for multiple rare variants association studies that incorporates sequencing qualities, Nucleic Acids Research, 2012 40 (8): e60. Software qMSAT 
  • Wei Wang, W Hu, F Hou, P Hu and Wei Z, SNVerGUI: a desktop tool for variant analysis of next-generation sequencing data, Journal of Medical Genetics, 2012 49 (12), 753-755. Software SNVerGUI
  • Wei Z, Wei Wang, et al, Large Sample Size, Wide Variant Spectrum, and Advanced Machine-Learning Technique Boost Risk Prediction for Inflammatory Bowel Disease, American Journal of Human Genetics, 2013 92 (6), 1008-1012.
  • Zhao Z, Wang Wei, and Wei Z. An empirical Bayes testing procedure for detecting variants in analysis of next generation sequencing data. Annals of Applied Statistics, 2013 7 (4), 2229-2248. , Supplementary Material (Technical Proof). Software ebVariant
  • Wang Wei, and Wei Z, Collapsing singletons may boost signal for associating rare variants in sequencing study. BMC Proceedings, 2014 8(Suppl 1):S50
  • Wang Wei, Wei Z, and Li H, A change-point model for identifying 3’UTR switching by next-generation RNA sequencing. Bioinformatics, 2014 30(15):2162-2170. Software UTR
  • Christopher Ochs, James Geller, Yehoshua Perl, Yan Chen, Junchuan Xu, Hua Min, James T Case, and Wei Z, Scalable quality assurance for large SNOMED CT hierarchies using subject-based subtaxonomies, Journal of the American Medical Informatics Association, 2015 22(3):507-518.
  • Sun Wenguang and Wei Z, Hierarchical Recognition of Sparse Patterns in Large-scale Simultaneous Inference, Biometrika, 2015 102(2):267-280.
  • Xiang Ji, Soon Ae Chun, Wei Z, and James Geller, Twitter sentiment classification for measuring public health concerns, Social Network Analysis and Mining, 2015 5:13.
  • Fei Tan, Yongxiang Xia, and Wei Z, Robust-yet-fragile nature of interdependent networks, Physical Review E, 2015 91(5):052809. 
  • Jie Zhang, Wei Z, Z. Yan,and A. Pani, Collaborated Online Change-point Detection in Sparse Time Series for Online Advertising, Proceedings of IEEE International Conference on Data Mining (ICDM), Atlantic City, NJ, November 2015; acceptance rate 18.1%.
  • Jie Zhang, Wei Z, An empirical Bayes change-point model for identifying 3' and 5' alternative splicing by next-generation RNA Sequencing, Bioinformatics, 2016 32:1823-1831. Software EBChangePoint
  • K. Zhang, S. Zhe, Chaoran Cheng, Wei Z, Z. Chen, H. Chen, G. Jiang, Y. Qi and J. Ye, Annealed Sparsity via Adaptive and Dynamic Shrinking, The 22nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2016), Pages 1325-1334, San Francisco, CA, August 2016; full paper, acceptance rate 70/784=8.9%.
  • Jie Zhang, Kuang Du, Ruihua Cheng, Wei Z, C. Qin, H. You, and S. Hu, Reliable Gender Prediction Based on Users' Video Viewing Behavior, Proceedings of IEEE International Conference on Data Mining (ICDM), Barcelona, Spain, December 2016; regular paper, acceptance rate 8.5%.
  • Fei Tan, Chaoran Cheng, Wei Z, Modeling Real Estate for School District Identification, Proceedings of IEEE International Conference on Data Mining (ICDM), Barcelona, Spain, December 2016; acceptance rate 19.6%.
  • Turki Turki, Wei Z., A Link Prediction Approach to Cancer Drug Sensitivity Prediction, International Conference on Intelligent Biology and Medicine (ICIBM), Houston, Texas, Dec 8-10, 2016.
  • Turki Turki, Wei Z., A Noise-Filtering Approach for Cancer Drug Sensitivity Prediction, NIPS 2016 Workshop on Machine Learning for Health, Barcelona, Spain, Dec 9, 2016.
  • Jie Zhang, Z. Zhao, K. Zhang, Wei Z, A Feature Sampling Strategy for Analysis of High Dimensional Genomic Data, The Fifteenth Asia Pacific Bioinformatics Conference (APBC 2017), Shenzhen, China, January 16-18 2017, journal version published in IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2019 16(2):434-441 .
  • Turki Turki, Wei Z, J. Wang, Transfer Learning Approach via Procrustes Analysis and Mean Shift for Cancer Drug Sensitivity Prediction, The 28th International Conference on Genome Informatics Workshop (GIW)/BIOINFO 2017, Seoul, Korea, Oct 31-Nov 3, 2017, journal version published in Journal of Bioinformatics and Computational Biology, 16(03) 2018.
  • Fei Tan, Chaoran Cheng, Wei Z, Modeling Time-aware Latent Hierarchical Model for Predicting House Prices, Proceedings of IEEE International Conference on Data Mining (ICDM), New Orleans, USA, November 18-21 2017; acceptance rate 19.9%.
  • Turki Turki, Wei Z, JTL Wang, Transfer Learning Approaches to Improve Drug Sensitivity Prediction in Multiple Myeloma Patients, IEEE Access, 2017 5:7381-7393.
  • Jie Zhang, Wei Z, Z Yan, MC Zhou, A Pani, Online change-point detection in sparse time series with application to online advertising, IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2017 49(6):1141-1151.
  • J Chen, E King, R Deek, Wei Z, Y Yu, D Grill, K Ballman, An omnibus test for differential distribution analysis of microbiome sequencing data, Bioinformatics, 2018 34(4):643-651.
  • Jie Zhang, Wei Z, J Chen, A distance-based approach for testing the mediation effect of the human microbiome, Bioinformatics, 2018 34(11):1875-1883.
  • Fei Tan, Kuang Du, Wei Z, Haoran Liu, C.Qin, R. Zhu, Modeling Item-specific Effects for Video Click, SIAM: SIAM International Conference on Data Mining (SDM18), San Diego, USA, May 3-5, 2018, acceptance rate 23.2%.
  • Ruihua Chen, Wei Z, K Zhang, Network Inference from Contrastive Groups Using Discriminative Structural Regularization, SIAM: SIAM International Conference on Data Mining (SDM18), San Diego, USA, May 3-5, 2018, acceptance rate 23.2%.  
  • Xin Gao, Jie Zhang, Wei Z, H Hakonarson, DeepPolyA: A Convolutional Neural Network Approach for Polyadenylation Site Prediction, IEEE Access, 2018 6:24340-24349.
  • Liulin Yang, N. Huang, Wei Z, Dual M-Convex Variable Subsets Family and Extremum Analysis for the OPF Problem, IEEE Access, 2018 6: 27018 - 27027
  • Tian Tian, Wei Z et al, The Long Noncoding RNA Landscape in Amygdala Tissues from Schizophrenia Patients, EBioMedicine, 2018 34:171-181
  • Tian Tian, J Wan, Y Han, H Liu, F Gao, Y Pan, Q Song, Wei Z, A Comprehensive Survey of Immune Cytolytic Activity-Associated Gene Co-Expression Networks across 17 Tumor and Normal Tissue Types, Cancers, 2018 10(9):307
  • Fei Tan, Xiurui Hou, Jie Zhang, Wei Z, Z. Yan, A Deep Learning Approach to Competing Risks Representation in Peer-to-Peer Lending, IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2019 30(5):1565-1574
  • Fei Tan, Wei Z, et al. A Blended Deep Learning Approach for Predicting User Intended Actions, Proceedings of IEEE International Conference on Data Mining (ICDM), Singapore, November 17-18 2018; regular paper, acceptance rate 84/948=8.86%.
  • X Xiong, W Wu, N Li, L Yang, Jie Zhang, Wei Z, Risk-Based Multi-Objective Optimization of Distributed Generation Based on GPSO-BFA Algorithm, IEEE Access, 2019 7:30563-30572.
  • Tian Tian, J Wan, Q Song, Wei Z, Clustering single-cell RNA-seq data with a model-based deep learning approach, Nature Machine Intelligence, 2019 1:191-198
  • Chaoran Cheng, Fei Tan, Xiurui Hou, Wei Z, Success Prediction on Crowdfunding with Multimodal Deep Learning, Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI), Macao, China, August 10-16 2019; acceptance rate 850/4752=17.89%.
  • Fei Tan, Wei Z, A. Pani, and Z. Yan, User Response Driven Content Understanding with Causal Inference, Proceedings of IEEE International Conference on Data Mining (ICDM), Beijing, China, November 8-11 2019; acceptance rate 194/1046 =18.5%.
  • Chaoran Cheng, Fei Tan, Wei Z, DeepVar: An End-to-End Deep Learning Approach for Genomic Variant Recognition in Biomedical Literature, Proceedings of the 34th AAAI Conference on Artificial Intelligence (AAAI-20), New York City, USA, Feb 7-12 2020; acceptance rate 1591/7737=20.6%.
  • H Yu, Q Li, Y Geng, Y Zhang, Wei Z, AirNet: A Calibration Model for Low-Cost Air Monitoring Sensors Using Dual Sequence Encoder Networks, Proceedings of the 34th AAAI Conference on Artificial Intelligence (AAAI-20), New York City, USA, Feb 7-12 2020; acceptance rate 1591/7737=20.6%.
  • Xiurui Hou, K Wang, Jie Zhang, Wei Z, An Enriched Time-Series Forecasting Framework for Long-Short Portfolio Strategy, IEEE Access, 2020 8:31992-32002.
  • Fei Tan, Tian Tian, Xiurui Hou, X Yu, L Gu, F Mafra, BD Gregory, Wei Z, et al, Elucidation of DNA methylation on N6-adenine with deep learning, Nature Machine Intelligence, 2020 2(8):466-475 news.
  • Xiurui Hou, K Wang, Cheng Zhong, Wei Z, ST-Trader: A Spatial-Temporal Deep Neural Network for Modeling Stock Market Movement, IEEE/CAA Journal of Automatica Sinica, 2021 8(5):1015-1024.
  • Tian Tian, Ruihua Cheng, Wei Z. An empirical bayes change point model for transcriptome time course data. Annals of Applied Statistics, 2021 15(1):509-526, EBtimecourse.
  • Tian Tian, M.R. Min, Wei Z. Model-based autoencoders for imputing discrete single-cell RNA-seq data. Methods, 2021 192:112-119.
  • Tian Tian, Jie Zhang, Xiang Lin, Wei Z, H Hakonarson. Model-based deep embedding for constraint clustering analysis of single cell RNA-seq data. Nature Communications, 2021 12:1873.
  • JT Glessner, Xiurui Hou, Cheng Zhong, Jie Zhang, et al, Wei Z, DeepCNV: a deep learning approach for authenticating copy number variations, Briefings in Bioinformatics, 2021 22(5):bbaa381.
  • Kuang Du, S Wei, Wei Z , et al. Pathway Signatures Derived from On-treatment Tumor Specimens Predict Response to Anti-PD1 Blockade in Metastatic Melanoma. Nature Communications, 2021 12:6023.
  • Xiang Lin, Haoran Liu, Wei Z, et al, An active learning approach for clustering single-cell RNA-seq data, Laboratory Investigation, 2022 102(3):227-235. 2022 102 (3), 227-235.
  • Xiang Lin, Le Gao, Nathan Whitener, Ashley Ahmed, Wei Z. A model-based constrained deep learning clustering approach for spatially resolved single-cell data. Genome Research, 2022 32:1906-1917.
  • Xiang Lin, Tian Tian, Wei Z, et al. Clustering of single-cell multi-omics data with a multimodal deep learning method. Nature Communications, 2022 13:7705. 
  • Tian Tian, Cheng Zhong, Xiang Lin, Wei Z, et al. Complex hierarchical structures in single-cell genomics data unveiled by deep hyperbolic manifold learning, Genome Research, 2023  33:232-24.
  • Yunpeng Xu, W Guo, Wei Z, Conformal Risk Control for Ordinal Classification, Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence (UAI), PMLR, 2023, 216:2346-2355.
  • Cheng Zhong, Tian Tian, Wei Z, Hidden Markov random field models for cell-type assignment of spatially resolved transcriptomics, Bioinformatics, 2023  39(11):btad641.
  • Tian Tian, Jie Zhang, Xiang Lin, Wei Z, et al. Dependency-aware deep generative models for multitasking analysis of spatial omics data, Nature Methods, 2024  21:1501-1513.
  • Wei Z, Discrete latent embeddings illuminate cellular diversity in single-cell epigenomics, Nature Computational Science, 2024  4:316-317.
  • Xiang Lin, Siqi Jiang, Le Gao, Wei Z, et al. MultiSC: a deep learning pipeline for analyzing multiomics single cell data, Briefings in Bioinformatics, 2024, accepted.