Yuanyue Li

Yuanyue Li
李渊越

ZJU 100 Young Professor

Zhejiang University

👋 Hello

I am Yuanyue Li, a bioinformatics scientist with a passion for understanding the molecular mechanisms of life by combining experimental and computational methods. I grew up and earned my doctoral degree in China, and then spent several enriching years working in Germany and the United States. Currently, I am leading a research group at Zhejiang University in China. In my spare time, I enjoy exploring the world with my wife and our two daughters.

Metabolites are the foundation of biological activity, playing crucial roles in processes such as energy production, signaling, and the regulation of cellular functions. Understanding their composition and dynamics is essential for deciphering the complex biochemical networks that drive cellular behavior. Mass spectrometry is a powerful tool for measuring molecules with high sensitivity and accuracy. Therefore, I use mass spectrometry as a primary tool to analyze metabolites. I have developed several methods to enhance the capabilities of mass spectrometry in life science research. Feel free to visit my GitHub page to explore the tools I’ve created.

For more information about my research, please visit my lab’s website: https://LiLab.Cool. I am actively seeking talented research assistants, graduate students, and postdoctoral researchers to join my team. If you are interested in my work, don’t hesitate to send me an email—I look forward to hearing from you!

Interests
  • Mass Spectrometry
  • Metabolomics
  • Biochemistry
  • Molecular Biology
Education
  • Ph.D. in Biochemistry and Molecular Biology, 2014

    Xiamen University

  • B.Sc. in Life Science, 2008

    Xiamen University

Featured Projects

Spectral Entropy & Entropy Similarity
By considering an MS/MS spectrum as a probability distribution, we introduced the concept of Spectral Entropy to evaluate the information within the spectrum. Expanding on this idea, we proposed Entropy Similarity as a metric to measure the similarity between two spectra. Utilizing this approach can lead to a reduction in the false positive rate for metabolite identification by up to 40%.
A video introduction to Spectral Entropy and Entropy Similarity can be found here.
Group-DIA
Group-DIA can analyze multiple DIA data files simultaneously, notably enhancing protein identification by grouping various mass spectrometry datasets.

Recent Publications

(2021). Spectral entropy outperforms MS/MS dot product similarity for small-molecule compound identification. Nature Methods.

Cite DOI URL Read Video

(2021). Coupling proteomics and metabolomics for the unsupervised identification of protein–metabolite interactions in Chaetomium thermophilum. PLOS ONE.

Cite DOI URL

(2015). Group-DIA: analyzing multiple data-independent acquisition mass spectrometry data files. Nature Methods.

Cite DOI URL