Dr Kai Cheng

(he/his)

Group Leader

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My work as a computational biologist sits at the exciting intersection of metaproteomics and artificial intelligence, with a core focus on advancing microbiome research.

After earning my PhD from the Chinese Academy of Sciences in 2015, where I created new proteomics data analysis tools, I moved to the University of Ottawa. There, my research integrated mass spectrometry, bioinformatics and AI to decode the complex relationships between microbes and their human hosts, culminating in a comprehensive metaproteomics platform for in-depth analysis of the human gut microbiome.

Since joining the Quadram Institute in 2025 as a Group Leader, I’ve been leading the AI & Metaproteomics for Human Gut Health group. Our mission is to transform personalised medicine and nutrition by developing centralised multi-omics repositories and cutting-edge AI frameworks. Ultimately, I aim to create Artificial Intelligence Virtual Cells (AIVCs) which offer a revolutionary way to simulate gut microbial ecosystems and understand their vital role in human wellbeing.

Key publications

Cheng, K.; Ning, Z.; Zhang, X.; Li, L.; Liao, B.; Mayne, J.; Stintzi, A.; Figeys, D. MetaLab: an automated pipeline for metaproteomic data analysis. Microbiome 5, 157 (2017). DOI: 10.1186/s40168-017-0375-2

Cheng, K.; Ning, Z.; Zhang, X.; Mayne, J.; Figeys, D. Separation and characterization of human microbiomes by metaproteomics. TrAC Trends in Analytical Chemistry 108, 221-230. DOI: 10.1016/j.trac.2018.09.006

Cheng, K.; Ning, Z.; Zhang, X.; Li, L.; Liao, B.; Mayne, J.; Figeys, D. MetaLab 2.0 Enables Accurate Post-Translational Modifications Profiling in Metaproteomics. Journal of the American Society for Mass Spectrometry 2020 31 (7), 1473-1482. DOI: 10.1021/jasms.0c00083

Cheng, K.; Ning, Z.; Li, L.; Zhang, X.; Serrana, J.M.; Mayne, J.; Figeys, D. MetaLab-MAG: A Metaproteomic Data Analysis Platform for Genome-Level Characterization of Microbiomes from the Metagenome-Assembled Genomes Database. Journal of Proteome Research 2023 22 (2), 387-398. DOI: 10.1021/acs.jproteome.2c00554

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