BIMSA > Zhong Wang
Professor Zhong Wang

Zhong Wang

Professor
Affiliation: BIMSA
Research Field: Biostatistics and Bioinformatic
Office: A3-2-205
Email: wangzhong@bimsa.cn

Biography

Zhong Wang, Ph.D. in Engineering, Research Professor. He received his bachelor’s degree in computer science and his doctoral degree in computational mechanics from Dalian University of Technology in 1994 and 2000, respectively. Since 2008, his research has focused on model construction, computational analysis, and software development, with long-term engagement in the fields of biostatistics and bioinformatics. In recent years, he has achieved a series of significant research outcomes in gene association analysis, gene regulation, and related areas of bioinformatics, publishing over 100 academic papers, including several in top international journals such as Nature Genetics and Nature Cancer. Currently, he is collaborating extensively with international research institutions on computational genomics and medical image processing using deep learning models. Outstanding Ph.D. candidates and postdoctoral researchers are welcome to join his team.

Research Interest

  • Artificial Intelligence for Science (Biology, Mathematics)
  • Computational Biology & Bioinformatics
  • Medical Image Processing

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

  • 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 - 2015 | Beijing Forestry University, China | Lecturer
  • 2008 - 2012 | Penn State College of Medicine, USA | Postdoc | Statistical Genetics

Honors and Awards

  • 2021 | LiaoNing Revitalization Leading Talents of LiaoNing Province
  • 2017 | Science and Technology Progress Award of Beijing Government for Genetic Mapping (Third prize)

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, J Zhang, Response to letter regarding “A deep learning approach for gastroscopic manifestation recognition based on Kyoto Gastritis Score”, Annals of Medicine, 57(1), 2561801 (2025)
  • [3] X Fan, G Zhang, P Ganzhang, G Pei, Q Zhao, S Bao, Z Zhao, Z Wang, EEG-Based Seizure Detection and Type Classification with Structured State Space Modeling and Graph Neural Networks, IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (2025)
  • [4] X Fan, Z Jia, Y Han, R Geng, X Li, Z Wang, Multi-Stain Attention Multiple Instance Learning for Prognosis Prediction in Esophageal Squamous Cell Carcinoma, IEEE International Conference on Bioinformatics and Biomedicine (2025)
  • [5] Z Xu, H Yang, X Fan, Z Wang, Evo2-Virus: Ultra-Short Viral Sequence Identification with EVO2, IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (2025)
  • [6] Z Zhang, L Jia, Z Xu, Z He, Z Wang, X Fan, MambaHM: High-Resolution Histone Modification Prediction from ATAC-seq and DNA Sequence, IEEE International Conference on Bioinformatics and Biomedicine (2025)
  • [7] W Yang, Y Han, Y Wang, X Fan, Z Wang, A High-Resolution Fine Localization Algorithm Integrating YOLO and SAM for Rounded-Corner Label Bounding Boxes, International Conference on Image Processing, Computer Vision (2025)
  • [8] X Fan, J Wang, Z Wang, Z Huang, W Yang, Z Wang, MSFF-SNet: An End-to-end Object Sorting Model with Multi-head Self-attention and Multi-scale Feature Fusion, 2025 International Joint Conference on Neural Networks (IJCNN), 1-8 (2025)
  • [9] X Fan, H Xue, Y Feng, Q Zhao, Z Zhao, Z Wang, Visual Feature Learning from Randomized EEG Trials for Object Recognition, IEEE International Conference on Multimedia and Expo (ICME), 1-6 (2025)
  • [10] Z Xu, J Zhong, W Wen, Y Bao, X Fan, X Liu, Z Wang, XGBoost6mA: A Framework for 6mA Site Prediction Based on Deep Learning and XGBoost, 2025 International Joint Conference on Neural Networks (IJCNN), 1-8 (2025)
  • [11] 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)
  • [12] 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)
  • [13] 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)
  • [14] 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)
  • [15] Z Wang, T Chu, LA Choate, CG Danko, Identification of regulatory elements from nascent transcription using dREG, Genome research, 29(2), 293-303 (2019)
  • [16] 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: 2026-06-19 18:00:10