BIMSA > Zhong Wang
Professor Zhong Wang

Group: Applied Statistics and Data Science

Office: A3-2-205

Email: zhongwang@bimsa.cn

Research Field: Biostatistics and Bioinformatic

Research Interest

  • Artificial Intelligence for Science (Biology, Mathematics)
  • Computational Biology & Bioinformatics
  • Medical Images
  • Software Development for Computational Biology & Bioinformatics

Education Experience

  • 1996 - 2000      Dalian University of Technology      Computational Mechanics      Ph.D
  • 1994 - 1996      Dalian University of Technology      Computer Science      Master
  • 1990 - 1994      Dalian University of Technology      Computer Science      Bachelor

Work Experience

  • 2025 -      School of Software Technology, Dalian University of Technology, China      Professor
  • 2019 - 2025      School of Software Technology, Dalian University of Technology, China      Professor
  • 2018 - 2019      College of Veterinary Medicine, Cornell, USA      Research Associate
  • 2015 - 2018      College of Veterinary Medicine, Cornell, USA      Postdoc      Bioinformatics
  • 2013 - 2024      Penn State College of Medicine, USA      Postdoc      Biostatistics
  • 2013 - 2015      Beijing Forestry University, China      Lecturer
  • 2008 - 2012      Penn State College of Medicine, USA      Postdoc      Statistical Genetics
  • 2003 - 2008      E-Connector Co. Ltd, Tokyo, Japan      Director
  • 2000 - 2002      GKT Co. Ltd, Tokyo, Japan      Director
  • 2000 - 2000      Information School, Dalian University      Lecturer

Honors and Awards

  • 2022      High-level Talents in Dalian
  • 2021      LiaoNing Revitalization Leading Talents of LiaoNing Province
  • 2017      Science and Technology Progress Award of Beijing Government for Genetic Mapping (Third prize)
  • 1997      Technology Advancement Award of Liaoning Government for Vector-Time Spatial Electrocardiogram (Third prize)
  • 1994      Technology Advancement Award of Dalian government

Publication

  • [1] Z Wang, C Danko, Z Zhang, X FAN, J Zhong, L Jia, Y Han, C Yang, Z He, ..., An end-to-end generalizable deep learning framework to comprehensively analyze transcriptional regulation (2025)
  • [2] X Fan, Z Wang, Expanding the horizon of interaction modeling in complex systems comment on" Topological change of soil microbiota networks for forest resilience under global warming" by …, Physics of Life Reviews, 53, 279-280 (2025)
  • [3] W Wen, J Zhong, Z Zhang, L Jia, T Chu, N Wang, CG Danko, Z Wang, dHICA: a deep transformer-based model enables accurate histone imputation from chromatin accessibility, Briefings in Bioinformatics, 25(6), bbae459 (2024)
  • [4] T Chu, Z Wang, D Pe’er, CG Danko, Cell type and gene expression deconvolution with BayesPrism enables Bayesian integrative analysis across bulk and single-cell RNA sequencing in oncology, Nature Cancer, 3(4), 505-517 (2022)
  • [5] Z Wang, AG Chivu, LA Choate, EJ Rice, DC Miller, T Chu, SP Chou et al., Prediction of histone post-translational modification patterns based on nascent transcription data, Nature Genetics, 54(3), 295-305 (2022)
  • [6] Z Wang, N Wang, Z Wang, L Jiang, Y Wang, J Li, R Wu, was: how to compute longitudinal GWAS data in population designs, Bioinformatics, 36(14), 4222-4224 (2020)
  • [7] Z Wang, T Chu, LA Choate, CG Danko, Identification of regulatory elements from nascent transcription using dREG, Genome research, 29(2), 293-303 (2019)
  • [8] Z Zhang, X Fan, J Zhong, L Jia, Y Han, C Yang, Z He, X Li, ST Yau, R Wu, ..., An end-to-end generalizable deep learning framework

 

Update Time: 2025-10-13 17:00:10