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Dingjie Wang

Principle Investigator

Education

Ph.D. in Computational Mathematics

Wuhan University
2014 - 2019

B.S. in Mathematics and Applied Mathematics

Changsha University of Science and Technology
2009 - 2013

Languages

  • Chinese (Native)
  • English (Professional)

About Theme

Basic Information

I am a Tenure-track Associate Professor and Principle Investigator in Institute for Math & AI, Wuhan University. I obtained Ph.D. (2019) in Computational Mathematics from School of Mathematics and Statistics, Wuhan University, supervised by Prof. Xiufen Zou. My dissertation is titled “Integration and quantitative analysis of high-dimensional biomedical data based on multi-scale networks”. During graduate stage, I used to be a visiting scholar in Department of Internal Medicine, University of Iowa (2017-2018) and Department of Biomedical Informatics, The Ohio State University (2018-2019), respectively. After graduation, I started my postdoc at The Ohio State University and University of Michigan, Ann Arbor, under the supervision of Prof. Kin Fai Au, from 2019 to 2024. Prior to joining Wuhan University, I am a Research Fellow in Department of Applied Mathematics at The Hong Kong Polytechnic University.

I am actively seeking highly motivated Postdoc, PhD and Master students from Mathematic, Statistic, Computer Science, Bioinformatics, or similar fields. Contact me (djwang@whu.edu) if you are interested, please include your CV and brief research statement.

Research Interests

I am primarily engaged in interdisciplinary research involving mathematics, machine learning, and biomedical sciences. My research encompasses areas such as bioinformatics, computational systems biology, and the theory and application of tensor decomposition. Currently, my primary research interest lies in developing innovative algorithms and statistical models to perform reliable quantitative and function analyses of gene isoforms and Transposable Elements (TEs) using cutting-edge sequencing techniques from PacBio and Oxford Nanopore Technologies. Our studies are anticipated to provide an efficient bioinformatics platform for improve our understanding of gene isoforms and TEs with complex biomedical context in a comprehensive manner. The specific topics I have examined include:

  • Develop statistical models for identifying and quantifying gene isoforms and TEs.
  • Design long-read-based pipeline to reveal landscape of zygotic TE activation during early embryogenesis in zebrafish.
  • Construction of gene/isoform regulatory networks using diverse genomic data.
  • Design network-based computational framework to predict and differentiate functions among gene isoforms.
  • Develop tensor-based indicators to identify key components in complex gene/isoform regulatory networks.

Research Experience

Tenure-track Associate Professor and Principle Investigator

2025.02 - present
Institute for Math & AI, Wuhan University, Wuhan, China

Research Fellow

2024.10 - 2025.01
Department of Applied Mathematics, Hong Kong Polytechnic University, Hong Kong, China

Postdoctoral Fellow

2023.02 - 2024.04
Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, USA

Postdoctoral Fellow

2019.06 - 2023.01
Department of Biomedical Informatics, The Ohio State University, USA

Visiting Scholar

2018.10 - 2019.06
Department of Biomedical Informatics, The Ohio State University, USA

Visiting Scholar

2017.11 - 2018.10
Department of Internal Medicine, University of Iowa, USA

Publications

*Co-first author, †Co-corresponding author

  • Improving gene isoform quantification with miniQuant
    • Haoran Li*, Dingjie Wang*, Qi Gao*, Puwen Tan*, Yunhao Wang*, Xiaoyu Cai*, Aifu Li, Yue Zhao, Andrew L. Thurman, Seyed Amir Malekpour, Ying Zhang, Roberta Sala, Andrea Cipriano, Chia-Lin Wei, Vittorio Sebastiano, Chi Song, Nancy Ruonan Zhang, Kin Fai Au†
    Nature Biotechnology, In Press, 2025.
  • Zygotic activation of transposable elements during zebrafish early embryogenesis
    • Bo Li*, Ting Li*, Dingjie Wang*, Ying Yang, Puwen Tan, Yunhao Wang, Yungui Yang†, Shunji Jia† and Kin Fai Au†
    Nature Communications, 16, Article number: 3692, 2025.
  • Systematic assessment of long-read RNA-seq methods for transcript identification and quantification
    • Francisco Pardo-Palacios*, Dingjie Wang*, Fairlie Reese*, Mark Diekhans*, Sílvia Carbonell-Sala*, Brian Williams*, ..., Christopher Vollmers†, Adam Frankish†, Kin Fai Au†, Gloria M. Sheynkman†, Ali Mortazavi†, Ana Conesa†, Angela N. Brooks†
    Nature Methods, 21:1349–1363, 2024.
  • A network-based computational framework to predict and differentiate functions for gene isoforms using exon-level expression data
    • Dingjie Wang, Xiufen Zou†, Kin Fai Au†
    Methods, 189:54-64, 2021.
  • Tensor-based mathematical framework and new centralities for temporal multilayer networks
    • Dingjie Wang, Wei Yu†, Xiufen Zou†
    Information Sciences, 512:563-580, 2020.
  • A new centrality measure of nodes in multilayer networks under the framework of tensor computation
    • Dingjie Wang, Xiufen Zou
    Applied Mathematical Modelling, 54:46-63, 2018.
  • Trajectory control in nonlinear networked systems and its applications to complex biological systems
    • Suoqin Jin*, Dingjie Wang*, Xiufen Zou†
    SIAM Journal on Applied Mathematics, 78(1):629-649, 2018.
  • Identifying key nodes in multilayer networks based on tensor decomposition
    • Dingjie Wang, Haitao Wang, Xiufen Zou
    Chaos, 27(6):063108, 2017.
  • Control energy and controllability of multilayer networks
    • Dingjie Wang, Xiufen Zou
    Advances in Complex Systems, 20(04n05):1750008, 2017.
  • Crosstalk between pathways enhances the controllability of signalling networks
    • Dingjie Wang, Suoqin Jin, Xiufen Zou
    IET Systems Biology, 10(1):2-9, 2016.
  • Estimation of control energy and control strategies for complex networks
    • Dingjie Wang, Suoqin Jin, Fang-xiang Wu, Xiufen Zou
    Advances in Complex Systems, 18(07n08):1550018, 2015.
  • Control of multilayer biological networks and applied to target identification of complex diseases
    • Wei Zheng, Dingjie Wang, Xiufen Zou
    BMC bioinformatics. 20, 271, 2019.
  • Optimization of controllability and robustness of complex networks by edge directionality
    • Man Liang, Suoqin Jin, Dingjie Wang, Xiufen Zou
    The European Physical Journal B, 89:186, 2016.

    Submitted

    *Co-first author, †Co-corresponding author

  • Tensor-based multiscale mathematical framework and centrality measures for temporal high-order networks
    • Dingjie Wang, Yijun Lou and Xiufen Zou
    SIAM Multiscale Modeling & Simulation, Under Review.

    Tool Development

    LRGASP - The Long-read RNA-seq Genome Annotation Assessment Project (LRGASP) Consortium is organizing a systematic evaluation of different methods for transcript computational identification and quantification using long-read sequencing technologies such as PacBio and Oxford Nanopore.
    miniQuant - A highly-accurate bioinformatics tool for transcript abundance estimation.
    miniSim - A realisitic long reads RNA-seq data simulator.
    IsoNet - A tool for inferring co-expression networks based on exon-level expression data.
    ENMNFinder - An easy and accessible user interface for the calculation and visualization of centralities in multilayer networks.

    Grants

    Fundamental theories and algorithms of biological big data analysis - National Overseas High-level Talent Program for Youth, Role: PI, 2025-2027.
    Quantitative and function analysis platform for repetitive genes and gene isoforms in pluripotency regulation and differentiations - Project number: R01HG011469, National Institutes of Health in USA, Role: Key project member, PI: Prof. Kin Fai Au, 2021-2025.
    Multilayer network modeling and optimal control of complex diseases - Project number: 61672388, Natural Science Foundation of China, Role: Key project member, PI: Prof. Xiufen Zou, 2017-2020.

    Invited Presentations

  • Machine learning-driven integration of long and short reads for accurate gene isoform quantification
    • 2025 Young Scholars Symposium on Data Science and Computational Systems Biology, Wuhan Institute of Technology, China, March 21-23, 2025.
  • LRGASP - Performance evaluation on Challenge 2
    • LongTREC project, Institute for Integrative Systems Biology (I2SysBio), Spain, March 09, 2024, Invited by Prof. Ana Conesa.
  • MiniQuant - Long reads improve isoform quantification by reducing the data deconvolution uncertainty
    • Department of Statistics, The Wharton School, University of Pennsylvania, USA, October 26, 2023, , Invited by Prof. Nancy Ruonan Zhang.
  • Opportunities and challenges of new long-read sequencing techniques in quantifying gene isoforms and transposable elements
    • The third Young Scholars Forum, School of Computer Science and Engineering, Central South University, China, August 23-25, 2023.
  • Systematic assessment of long-read RNA-seq methods for transcript identification and quantification - Challenge 2 Part
    • LRGASP Consortium Community Calls, USA, March 03, 2022.
  • Integration and quantitative analysis of multi-dimensional biomedical data based on multi-scale networks
    • China University of Geosciences, Wuhan, China, Junly 18, 2020.
  • Tensor-based mathematical framework and new centralities for temporal multilayer networks
    • Changsha University of Science and Technology, ChangSha, China, May 30, 2019.