Zhi Wei

Wei, Zhi
Associate Professor, Computer Science
3801 GITC
About Me

Dr. Zhi Wei receives his Ph.D. from the University of Pennsylvania and M.S. from the Rutgers University-New Brunswick. His research interests include statistical modelling, machine learning and data mining with applications to various fields including Bioinformatics, genetics, web analytics, media analytics, advertising et al. His methodology works have been published in prestigious journals and conferences including JASA, Biometrika, AJHG, AOAS, Bioinformatics, Biostatistics, PLoS Genetics, NAR, NIPS, KDD, ICDM et al. He is an editorial board member of PLoS ONE, Frontiers in Bioinformatics and Computational Biology, and Frontiers in Applied Genetic Epidemiology.

Courses Taught
  • CS636 Data Analytics with R Programming
  • BNFO615 Data Analysis in Bioinformatics
  • BNFO620 Genomic Data Analysis
Research Interests
  • Bioinformatics
  • Statistical Genetics
  • Statistical modeling
  • Machine Learning
  • Data Mining
  • 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, Wang K, Qu H, Zhang H, Bradfield J, Kim C, Frackleton E, Hou C, Glessner JT, Chiavacci R, Stanley C, Monos D, Grant SFA, Polychronakos C and Hakonarson H, 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.
  • 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, Jonathan Bradfield, Jin Li, Christopher Cardinale, Edward Frackelton, Cecilia Kim, Frank Mentch, Kristel Van Steen, Peter M. Visscher, Robert N. Baldassano, Hakon Hakonarson and the International IBD Genetics Consortium, 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, Zhenyu Yan,and Abhishek Pani, Collaborated Online Change-point Detection in Sparse Time Series for Online Advertising, Proceedings of IEEE International Conference on Data Mining (ICDM), 2015.
  • 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
  • Kai Zhang, Shandian Zhe, Chaoran Cheng, Wei Z, Zhengzhang Chen, Haifeng Chen, Guofei Jiang, Yuan Qi and Jieping Ye, Annealed Sparsity via Adaptive and Dynamic Shrinking, The 22nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2016), San Francisco, CA, August, 2016.