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Yue Chen

Yue Chen

· Associate Professor

University of Minnesota · Biochemistry, Molecular Biology, and Biophysics

Active 1988–2026

h-index83
Citations15.4k
Papers419156 last 5y
Funding$9.2M1 active

Academic metrics are sourced from OpenAlex and public funding records; values may differ from Google Scholar.

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About

Yue Chen, PhD, is an Associate Professor at the University of Minnesota Medical School, affiliated with the departments of Biochemistry, Molecular Biology & Biophysics. His long-term research goal is to discover and characterize novel posttranslational modification (PTM) pathways involved in protein homeostasis, cellular signaling, and epigenetic regulation. To achieve this, he develops and applies mass spectrometry-based quantitative and chemical proteomics technologies, along with bioinformatic analysis tools, for system-wide characterization of protein modification networks. His research employs both cellular and animal-based models to explore diverse biological applications, including cancer, metabolic diseases, nutrition, and aging. Dr. Chen's work focuses on understanding the roles of PTMs in various biological processes, contributing to the broader understanding of protein regulation and cellular function.

Research topics

  • Internal medicine
  • Virology
  • Medicine

Selected publications

  • Flexible circuits engineered for complex and extreme environments

    Soft Science · 2025-11-11 · 2 citations

    articleOpen access1st authorCorresponding

    Driven by the rising demand for mechanical adaptability, structural reconfigurability, and multifunctional integration, flexible circuits are gaining increasing importance in advanced electronic systems. They serve as key enablers for next-generation devices owing to their intrinsic mechanical compliance and stable electrical performance. In extreme environments, flexible circuits face severe reliability issues such as dielectric drift, interfacial delamination, crack propagation, and metal elec…

  • FMEnets: Flow, Material, and Energy networks for non-ideal plug flow reactor design

    ArXiv.org · 2025-05-10

    preprintOpen access

    We propose FMEnets, a physics-informed machine learning framework for the design and analysis of non-ideal plug flow reactors. FMEnets integrates the fundamental governing equations (Navier-Stokes for fluid flow, material balance for reactive species transport, and energy balance for temperature distribution) into a unified multi-scale network model. The framework is composed of three interconnected sub-networks with independent optimizers that enable both forward and inverse problem-solving. In…

Recent grants

Frequent coauthors

  • John Fassett

    University of Graz

    364 shared
  • Robert J. Bache

    321 shared
  • Xin Xu

    Shanghai University of Sport

    311 shared
  • Hongliang Li

    Beijing Tian Tan Hospital

    214 shared
  • Zhongbing Lu

    University of Chinese Academy of Sciences

    202 shared
  • Dongmin Kwak

    197 shared
  • Xinli Hu

    Capital Medical University

    174 shared
  • Ping Zhang

    160 shared

Labs

Awards & honors

  • Dr. James E. Rubin Medical Memorial Award
  • Graduating Medical Student Research Award
  • Veneziale-Steer Award
  • Dr. Marvin and Hadassah Bacaner Research Awards
  • Schmidt Steer Award

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