Biography

Currently, Lingyu Li is a Postdoctoral Fellow at The University of Hong Kong, where she collaborates with Dr. Yuanhua Huang on Bioinformatics. She received her Ph.D. at Shandong University, supervised by Prof. Zhi-Ping Liu. Furthermore, Lingyu was also a joint training Ph.D. student at The University of Hong Kong, supervised by Prof. Wai-Ki Ching.

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2025/07/11/Friday
04:48:07
Interests
  • Bioinformatics
  • Machine Learning
  • Biomarker Discovery
  • Spatial Transcriptomics
  • Single-cell Data Science
  • Sparse Statistical Learning
  • Computational Mathematics
Education
  • PhD in Biomedical Engineering, 2019.09-2023.06

    Shandong University

  • MSc in Computational Mathematics, 2016.09-2019.06

    Shandong Normal University

  • BSc in Mathematics and Applied Mathematics, 2012.09-2016.06

    Shandong Normal University

Skills

R

90%

Python

80%

Statistics

100%

Experiences

 
 
 
 
 
Post-doctoral Fellow in Bioinformatics
Aug 2023 – Present Hong Kong, China

Research project:

  • Multimodal AI decoding tumor-immune interaction by fusing spatial RNA-seq and histological images
 
 
 
 
 
PhD in Biomedical Engineering
Sep 2019 – Jun 2023 Jinan

Doctoral thesis title:

  • Biomarker discovery methods based on connected network regularized feature selection
 
 
 
 
 
Joint training PhD in Computational Biology
Dec 2021 – Mar 2023 Hong Kong, China

Research project:

  • Bioinformatics and optimization with applications in biomarker discovery and feature selection
 
 
 
 
 
Master of Computational Mathematics
Sep 2016 – Jun 2019 Jinan

Master thesis:

  • Numerical methods and theoretical analysis of a class of groundwater pollution problems
 
 
 
 
 
Bachelor of Mathematics and Applied Mathematics
Sep 2012 – Jun 2016 Jinan

Bachelor thesis:

  • Uniform convergence of function term series and its applications

Recent & Upcoming Conferences

2024 Human Cell Atlas (HCA) Asia Meeting
An opportunity to give a poster.

Publications

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(2025). NetWalkRank: Cancer driver gene prioritization in multiplex gene regulatory networks by a random walk approach. In * IEEE ACM T COMPUT BI*.

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(2023). LogBTF: gene regulatory network inference using Boolean threshold network model from single-cell gene expression data . In BIOINFORMATICS.

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(2021). Discovery of spontaneous preterm birth biomarkers based on machine learning. In Journal of Nanjing University(Natural Sciences).

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(2016). Uniform convergence and application of function term's series. In Journal of Shandong Normal University (Natural Science).

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