Publications
$ denotes equal contributions; ^ denotes corresponding author(s)
Xiong, X.$, Liu, Y.$, Pu, D., Yang, Z., Bi, Z., Tian, L.^, and Li, X.^ (2024). DeSide: A unified deep learning approach for cellular deconvolution of tumor microenvironment. Proc. Natl. Acad. Sci. U. S. A., 121, e2407096121. Article, Github
Zhou, Z., Luo, M., Chen, X., Yin, Y., Xiong, X., Wang, R., and Zhu, Z.-J^. (2020). Ion mobility collision cross-section atlas for known and unknown metabolite annotation in untargeted metabolomics. Nat. Commun. 11, 4334. Article
Shen, X., Wang, R., Xiong, X., Yin, Y., Cai, Y., Ma, Z., Liu, N., and Zhu, Z.-J^. (2019). Metabolic reaction network-based recursive metabolite annotation for untargeted metabolomics. Nat. Commun. 10, 1516. Article
Zhou, Z., Shen, X., Chen, X., Tu, J., Xiong, X., and Zhu, Z.J.^ (2019). LipidIMMS Analyzer: integrating multi-dimensional information to support lipid identification in ion mobility—mass spectrometry based lipidomics. Bioinformatics 35. Article
Zhou, Z., Tu, J., Xiong, X., Shen, X., and Zhu, Z.J.^ (2017). LipidCCS: prediction of collision cross-section values for lipids with high precision to support ion mobility–mass spectrometry-based lipidomics. Anal. Chem. 89, 9559–9566. Article
Zhou, Z., Xiong, X., and Zhu, Z.-J.^ (2017). MetCCS predictor: a web server for predicting collision cross-section values of metabolites in ion mobility-mass spectrometry based metabolomics. Bioinformatics 33, 2235–2237. Article