Yixuan Wang (王艺璇)
Yixuan Wang (王艺璇)
PhD candidate @CUHK

About

I am currently a final-year PhD candidate at Artificial Intelligence in Healthcare (AIH) group and Machine Intelligence and Social Computing (MISC) Lab of The Chinese University of Hong Kong, supervised by Prof. Yu Li and Prof. Irwin King. Before that, I received the Bachelor’s degree in Mathematics and Applied Mathematics from Harbin Institute of Technology in 2022.

My research interest includes computational challenges in biology and healthcare, especially that related to single-cell data — and, increasingly, building AI models that can simulate cells in silico.

 We're hiring — join us!

I'm co-founder & CTO of 华源智因（北京）生物科技有限公司, an early-stage company building AI for cell biology. We're hiring interns and full-time team members — undergrad, master's, PhD, and recent grads are all welcome; backgrounds in computer science, bioinformatics, medicine, or chemistry are a great fit. If this sounds like you, I'd love to hear from you — send your CV (or just a few lines about yourself) to vcell.hiring@gmail.com.

我是 华源智因（北京）生物科技有限公司 的联合创始人兼 CTO，公司方向是面向细胞生物学的 AI。现招 实习生 / 全职，欢迎本科、硕士、博士及应届毕业生；计算机、生信、医学、化学背景皆可。如果感兴趣，欢迎把简历（或几句自我介绍）发到 vcell.hiring@gmail.com，期待和你聊聊。
× Enlarged view

BibTeX Citation

× 

            
          

        

      

    







  

    

      
      Research Directions
    

    

      Across my PhD and my startup work, the through-line is the same — building AI that doesn't just
      read cells but models them. Here's the map of directions I care about.
    


    

      

        
        

        
        
        
        
        
        

        

          
          
          Virtual
Cell
        


        

          

            

              
              Foundation Models of the Cell
              Large multi-omics, cross-scale models of cellular state — and the benchmarks for them (see SCMBench).
            

          

          

            

              
              Perturbation Modeling
              Predicting how cells respond to genetic and chemical perturbations.
            

          

          

            

              
              Drug Design
              Designing molecules in silico from a model of how cells should respond.
            

          

          

            

              
              Development & Tissue Modeling
              How cells self-organize into tissues, organs, and embryos across scales.
            

          

          

            

              
              Agentic AI for Biology
              Autonomous agents that go from raw data to biological insight.
            

          

          

            

              
              AI for Precision Medicine
              Translating cell-level models into clinical tools — diagnosis, monitoring, treatment choice (see cfDecon).
            

          

        

      

    


    
    
      
  •  Foundation models of the cell — multi-omics & cross-scale (SCMBench)
    • 
      
  •  Perturbation modeling — cellular response to perturbations
    • 
      
  •  Drug design — generative, in silico
    • 
      
  •  Development & tissue modeling across scales
    • 
      
  •  Agentic AI for biology — from raw data to insight
    • 
      
  •  AI for precision medicine (cfDecon)
    • 
    

  









  

    
    Selected Publications
  

  
*: equal contribution

  Perturbation Modeling
  

    

      

        
      

      

        

          
          Predicting drug responses of unseen cell types through transfer learning with foundation models
        

        
        

          Yixuan Wang*,
          Xinyuan Liu*,
          Yimin Fan, 
          Binghui Xie, 
          James Cheng, 
          Kam Chung Wong, 
          Peter Cheung,
          Irwin King, and Yu Li

          
        


        

          Nature Computational Science
          
        

        

          [Paper]
        

      

    

  


  Cell State Understanding
    

    

      

        
        
      

      

        

          SCMBench: Benchmarking Domain-specific and Foundation Models for Single-cell Multi-omics Data Integration
        

        

          
        

        

          Yixuan Wang,
          Yimin Fan,
          Xuesong Wang,
          Tingyang Yu,
          Yongshuo Zong, 
          Xinyuan Liu, 
          Gaoyang Zhong, Meitong Liu,
          Qing Li, 
          Kin hei Lee, 
          Khachatur Dallakyan, 
          Zhichao Hu, 
          Yaqian Qi, 
          Junjie Huang,
          Gengjie Jia, 
          Jiao Yuan, 
          Ting-Fung Chan, 
          Xin Gao, 
          Irwin King, and Yu Li
        


        

          Nature Communications
          
        

        

          [Paper]
        

      

    


  

    

      

        
        
      

      

        

          cfDecon: Accurate and Interpretable methylation-based cell type deconvolution for cell-free DNA
        

        

          
        

        

          Yixuan Wang*,
          Jiayi Li*,
          Jingqi Li,
          Shen Yang,
          Yuhan Huang, 
          Xinyuan Liu, 
          Yimin Fan, 
          Irwin King, 
          Yumei Li, and Yu Li

          
        


        

          RECOMB 2025 
          
        

        

          [Paper]
          
        

      

    


    

      

        
      

      

        

          Deep autoencoder for interpretable tissue-adaptive deconvolution and cell-type-specific gene analysis
        

        

        

        

          Yanshuo Chen*,
          Yixuan Wang*,
          Yuelong Chen,
          Yuqi Cheng,
          Yumeng Wei,
          Yunxiang Li,
          Jiuming Wang,
          Yingying Wei,
          Ting-Fung Chan,
          and Yu Li
          
        


        

          Nature Communications
          
        

        

          [Paper]
        

      

    


    

      

        
      

      

        

          scNovel: A Scalable Deep Learning‑based Network for Novel Rare Cell Discovery in Single‑cell Transcriptomics
        

        

        

        

          Chuanyang Zheng*,
          Yixuan Wang*,
          Yuqi Cheng,
          Xuesong Wang,
          Hongxin Wei,
          Irwin King,
          and Yu Li
          
        


        

          Briefings in Bioinformatics
          
        

        

          [Paper]
        

      

    



  Others
    

      

        
      

      

        

          Accurate RNA 3D structure prediction using a language model-based deep learning approach
        

        

          
        

        

          Tao Shen*,
          Zhihang Hu*,
          Siqi Sun*,
          Di Liu*,
          Felix Wong,
          Jiuming Wang,
          Jiayang Chen,          
          Yixuan Wang,
          Liang Hong,
          Jin Xiao,
          Liangzhen Zheng,
          Tejas Krishnamoorthi,
          Irwin King,
          Sheng Wang,
          Peng Yin,
          James J. Collins,
          and Yu Li*
          

          
        


        

          Nature Methods
          
        

        

          [Paper]
          
        

      

    


    
  










  

    

      
      Selected Honors
    

    
    
    
      
  • RECOMB Travel Fellowship (10 each year globally), 2025.
    • 
      
  • Postgraduate scholarship of CUHK, 2022 ∼ now.
    • 
      
      
  • Most 10 of the Outstanding Undergraduate for international influence, 2022.
    • 
      
      
  • National Scholarship for performance in the academic year 2019-2020 (Individual), 2020.
    • 
      
      
      
      
  • International Finalist Winner in Mathematical Contest in Modeling-MCM (Leader), 2020.
    • 
      
    

    
  








  

    

      
      Research Experience
    

    
    GeneSight Co-founder & CTO

    Genebio.AI Research Intern

    BioMap Intelligent Technology Research Intern

  





      





  

  
  

    

      
    

  


  
Top