Do your best, and let the God do the rest.
I am now a postdoctoral research fellow at Stanford University. I am broadly interested in Metabolomics, Multi-omics, Biostatistics, Systems Biology, and Bioinformatics, and their application in healthcare.
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.
Enjoy the life.
I am made up of choices.
HONORS AND AWARDS
Student Travel Award
The International Metabolomics Society (2018)
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).
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)
Science can be interesting.
Metabolic Reaction Network-based Recursive Metabolite Annotation for Untargeted Metabolomics
MetFlow: An Interactive and Integrated Workflow for Metabolomics Data Cleaning and Differential Metabolite Discovery
Development of A Correlative Strategy to Discover Colorectal Tumor Tissue Derived Metabolite Biomarkers in Plasma Using Untargeted Metabolomics
LipidIMMS Analyzer: Integrating Multi-dimensional Information to Support Lipid Identification in Ion Mobility–Mass Spectrometry based Lipidomics
Assessment of The Response to Neoadjuvant Chemo-Radiation in Rectal Cancer Patients based on a Metabolomics Approach
LipidCCS: Prediction of Collision Cross-Section Values for Lipids with High Precision to Support Ion Mobility-Mass Spectrometry-based Lipidomics
Large-Scale Prediction of Collision Cross-Section Values for Metabolites in Ion Mobility - Mass Spectrometry
Serum Metabolomics for Early Diagnosis of Esophageal Squamous Cell Carcinoma by UHPLC-QTOF/MS
Normalization and Integration of Large-Scale Metabolomics Data Using Support Vector Regression
Software and algorithm development. More software in my Github.
Metabololite identification and dysregulated network analysis.
MetDNA characterizes initial seed metabolites using a small tandem spectral library, and utilize their experimental MS2 spectra as surrogate spectra to annotate their reaction-paired neighbor metabolites which are subsequently served as the basis for recursive analysis.
Normalization and Integration of Large-Scale Metabolomics Data Using Support Vector Regression.
We developed a machine learning algorithm-based method, support vector regression (SVR), for large-scale metabolomics data normalization and integration. The unwanted intra- and inter-batch variations can be effectively removed after SVR normalization.