Xin Xiong (熊昕)

Xin Xiong (熊昕)

Bioinformatics, machine learning, system biology

Posts by Collection

photograph

projects

publications

MetCCS

Published in Bioinformatics, 2017

Here, we developed the first web server, namely, MetCCS Predictor, for predicting CCS values. It can predict the CCS values of metabolites using molecular descriptors within a few seconds. Common users with limited background on bioinformatics can benefit from this software and effectively improve the metabolite identification in metabolomics.

Recommended citation: Zhou, Zhiwei, Xin Xiong, and Zheng-Jiang Zhu. "MetCCS predictor: a web server for predicting collision cross-section values of metabolites in ion mobility-mass spectrometry based metabolomics." Bioinformatics 33.14 (2017): 2235-2237. https://doi.org/10.1093/bioinformatics/btx140

LipidCCS

Published in Analytical chemistry, 2017

We developed a new approach, namely, LipidCCS, to precisely predict lipid CCS values.

Recommended citation: Zhou, Zhiwei, Jia Tu, Xin Xiong, Xiaotao Shen, and Zheng-Jiang Zhu. "LipidCCS: prediction of collision cross-section values for lipids with high precision to support ion mobility–mass spectrometry-based lipidomics." Analytical chemistry 89, no. 17 (2017): 9559-9566. https://doi.org/10.1021/acs.analchem.7b02625

MetDNA

Published in Nature Communications, 2019

MetDNA implements a metabolic reaction network (MRN) based recursive algorithm for metabolite identification, which supports data from different LC systems (e.g., HILIC and reverse phase) and MS platforms (e.g., Agilent QTOF, Sciex TripleTOF, Thermo Orbitrap, and others).

Recommended citation: Shen, Xiaotao, Ruohong Wang, Xin Xiong, Yandong Yin, Yuping Cai, Zaijun Ma, Nan Liu, and Zheng-Jiang Zhu. "Metabolic reaction network-based recursive metabolite annotation for untargeted metabolomics." Nature communications 10, no. 1 (2019): 1-14. https://doi.org/10.1038/s41467-019-09550-x

AllCCS

Published in Nature Communications, 2020

Here, we curate an ion mobility CCS atlas, namely AllCCS, and develop an integrated strategy for metabolite annotation using known or unknown chemical structures.

Recommended citation: Zhou, Zhiwei, Mingdu Luo, Xi Chen, Yandong Yin, Xin Xiong, Ruohong Wang, and Zheng-Jiang Zhu. "Ion mobility collision cross-section atlas for known and unknown metabolite annotation in untargeted metabolomics." Nature communications 11, no. 1 (2020): 1-13. https://doi.org/10.1038/s41467-020-18171-8

DeSide

Published in bioRxiv, 2023

We present methods to refine data simulation with real tumor data guidance to generate a modest training set and tailor a unified deep learning model to bypass the issue of highly variable gene expression in cancer cells.

Recommended citation: Xiong, X., Liu, Y., Pu, D., Yang, Z., Bi, Z., Tian, L., and Li, X. (2023). DeSide: A unified deep learning approach for cellular decomposition of bulk tumors based on limited scRNA-seq data. bioRxiv, 2023.05.11.540466. https://doi.org/10.1101/2023.05.11.540466

teaching

Teaching experience 1

Undergraduate course, University 1, Department, 2014

This is a description of a teaching experience. You can use markdown like any other post.

Teaching experience 2

Workshop, University 1, Department, 2015

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