• ABOUT ME

    Do your best, and let the God do the rest.

    Click to see my Shen Blog

    I am a phD student in Interdisciplinary Research Center on Biology and Chemistry (IRCBC), Shanghai Institute of Organic Chemistry (SIOC), Chinese Academy of Sciences (CAS). I am broadly interested in Metabolomics, Analytical chemistry, Biostatictics, Systems biology and Bioinformatics.

     

    My research focuses on the development of bioinformatic algorithms and tools for large-scale metabolomics as well as its application in discovering new biomarkers related to human diseases. I have developed several algorithms and tools in the past five years. The first algorithm was MetNormalizer, which is a machine learning-based algorithm for data normalization and integration of large-scale metabolomics. Then I developed a web-based tool, MetFlow , aiming to provide a comprehensive pipeline for data cleaning and statistical analysis of large-scale metabolomics. The most important algorithm I have developed is called MetDNA, which is a novel algorithm for metabolite identification and dysregulated pathway analysis in untargeted metabolomics.

     

  • Education

    I am made up of choices.

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    Chinese Academy of Sciences

    2013.9 - Present

    Ph.D. in Metabolomics and Bioinformatics

    Advisor: Dr. Zheng-Jiang Zhu

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    Inner Mongolia University

    2009.9-2013.7

    BSs in Biotechnology

  • research INTERESTS

    I like science, but science doesn't like me.

    Metabolomics

    Large-scale metabolomics data processing and analysis.

    R programming

    For data and science.

    Bioinformatics&Biostatistics

    Algorithm and software development.

    Biomarker Discovery

    Precision medicine for the better life.

  • HONORS AND AWARDS

    Student Travel Award

    The International Metabolomics Society (2018)

    Travel Award

    Journal Metabolites (2018)

    China National Scholarship

    Ministry of Education of the People’s Republic of China (2017)

    Award for Outstanding Report

    The 3th China Mass Spectrometry Analysis Conference (2017).

    Merit Student

    University of Chinese Academy of Sciences (2016)

    Award for Outstanding Report

    The 34th China Mass Spectrometry Society Conference (2016)

    Inner Mongolia Outstanding Graduate

    Inner Mongolia Autonomous Region (2013)

    National Encouragement Scholarship

    Ministry of Education of the People’s Republic of China (2011)

  • PUBLICATIONs

    Large-scale Metabolite Identification for Untargeted Metabolomics Using Metabolic Reaction Network.

    X. Shen, R. Wang, X. Xiong, Y. Yin, Y. Cai, J. Ma, N. Liu and Z.-J. Zhu, Nature Communications, 2018, in revision.

    MetFlow: An Interactive and Integrated Workflow for Metabolomics Data Cleaning and Differential Metabolite Discovery.

    X. Shen and Z.-J. Zhu, Bioinformatics, 2018, under review.

    LipidIMMS Analyzer: Integrating Multi-dimensional Information to Support Lipid Identification in Ion Mobility–Mass Spectrometry based Lipidomics.

    Z. Zhou, X. Shen, X. Chen, J. Tu, X. Xiong, and Z.-J. Zhu, Bioinformatics, 2018, in press. LINK

    Assessment of The Response to Neoadjuvant Chemo-Radiation in Rectal Cancer Patients based on a Metabolomics Approach.

    H. Jia, X. Shen (Co-first author), Y. Guan, M. Xu, M. Mo, J. Zhu and Z.-J. Zhu, Radiotherapy and Oncology, 2018, in press. LINK

    LipidCCS: Prediction of Collision Cross-Section Values for Lipids with High Precision to Support Ion Mobility-Mass Spectrometry based Lipidomics.

    Z. Zhou, J. Tu, X. Xiong, X. Shen, and Z.-J. Zhu, Analytical Chemistry, 2017, 89, 9559–9566. LINK

    Large-Scale Prediction of Collision Cross-Section Values for Metabolites in Ion Mobility - Mass Spectrometry.

    Z. Zhou, X. Shen, J. Tu, and Z.-J. Zhu, Analytical Chemistry, 2016, 88, 11084-11091. LINK

    Serum Metabolomics for Early Diagnosis of Esophageal Squamous Cell Carcinoma by UHPLC-QTOF/MS.

    J. Wang, T. Zhang, X. Shen (Co-first author), J. Liu, D. Zhao, Y. Sun, L. Wang, Y. Liu, X. Gong, Y. Liu, Z.-J. Zhu, F. Xue, Metabolomics, 2016, 12: 116. LINK

    Normalization and Integration of Large-Scale Metabolomics Data Using Support Vector Regression.

    X. Shen, X. Gong, Y. Cai, Y. Guo, J. Tu, H. Li, T.Zhang, J. Wang, F. Xue, and Z.-J. Zhu, Metabolomics, 2016, 12: 89. LINK

  • oral & poster presentations

    Metabolic Reaction Network-based Recursive Metabolite Identification for Untargeted Metabolomics. (oral)

    The 14th International Conference of the Metabolomics Society, 28th, June, 2018, Seattle, Washington, USA.

    Assessment of the Response to Neoadjuvant Chemo-Radiation in Rectal Cancer Patients based on a Metabolomics Approach.(oral)

    The Third China Mass Spectrometry Analysis Conference, December, 2017, Xiamen, Fujian, China.

    Normalization and Integration of Large-Scale Mass Spectrometry-based Metabolomics Data Using Support Vector Regression.(oral)

    The 34th China Mass Spectrometry Society Conference, September, 2016, Xining, Qinghai, China.

    Normalization and Integration of Large-Scale Mass Spectrometry-based Metabolomics Data Using Support Vector Regression.(oral)

    The 64th American Society for Mass Spectrometry Conference, June, 2016, San Antonio, Texas, USA.

    Metabolic Reaction Network-based Metabolite Annotation in Untargeted Metabolomics. (poster)

    The 13th International Conference of the Metabolomics Society, June, 2017, Brisbane, Austria.

  • Projects

    Software and algorithm development.

    Interactive and Integrated Workflow for Metabolomics Data Cleaning and Differential Metabolite Discovery
    Metabolic Reaction Network Based Metabolite Annotation and Dysregulated Network Analysis.
    Normalization for Large-scale Metabolomics Data Using Machine Learning Method.
    An Open Platform to Sharing Omics Information
  • reach out

    Please leave me a message if you want to talk with me.

    26, Qiuyue Road, Pudong New Area, Shanghai, China.
    +86 15821606469
  • GET IN TOUCH

    Please contact me via the social-network information.

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  • Get a copy of my CV. to know me better.