BIMSA > 王忠
研究员 王忠

团队: 概率, 应用统计和数据科学

办公室: A3-2-205

邮箱: wangzhong@bimsa.cn

研究方向: 生物统计和生物信息学

个人简介

王忠，工学博士，研究员。1994年和2000年分别毕业于大连理工大学，获计算机专业学士学位和计算力学专业博士学位。自2008年以来，他以模型构建、计算分析和软件开发为主要研究方向，长期从事生物统计学与生物信息学研究。近年来在基因关联分析、基因调控等生物信息学领域取得了一系列具有重要价值的研究成果，发表学术论文百余篇，其中多篇发表于 Nature Genetics、Nature Cancer 等国际顶级学术期刊。目前，他正基于深度学习模型，与多家国外科研机构在计算基因组学和医学图像处理等方向开展广泛合作。欢迎优秀博士研究生和博士后加入团队。

研究兴趣

  • AI4Sci (生物，数学)
  • 计算生物学和生物信息学
  • 医学图像处理

教育经历

  • 1996 - 2000      大连理工大学      计算力学      Ph.D
  • 1994 - 1996      大连理工大学      计算机科学      Master
  • 1990 - 1994      大连理工大学      计算机科学      Bachelor

工作经历

  • 2019 - 2025      大连理工大学软件学院      教授
  • 2018 - 2019      ‌康奈尔大学兽医学院      助理研究员
  • 2015 - 2018      ‌康奈尔大学兽医学院      博士后      生物信息
  • 2013 - 2015      北京林业大学      讲师
  • 2008 - 2012      宾州州立大学医学院      博士后      统计基因

荣誉与奖项

  • 2021      辽宁省“兴辽英才计划”海内外高层次人才引进集聚计划创新领军人才
  • 2017      北京市科学技术奖三等奖，林木数量性状基因解析理论与方法

出版物

  • [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

 

更新时间: 2026-03-31 14:00:11