For the intersection of Mass pectrometry and computational biomedicine

Mass spectrometry · Data Analysis and Software · Human Health/Disease

The MSCBM Lab is led by Prof. Hemi Luan (栾合密博士), PhD, and is a part of the School of Biomedical and Pharmaceutical Sciences,GDUT, China. We work at the intersection of Mass pectrometry and computational biomedicine.The broad goal of our research is to gain mechanistic insights about human health by using mass spectrometry.

• Things We work with

  • Mass spectrometry
    • [Environmental Chemical Exposomics and Metabolomics]
    • [Software]
  • Computational biomedicine

• Job, Come and join us

• Publication

Here you can read more about the three main research topics we have at the moment:

• The advent of Mass spectrometry (MS)-based analytical technologies has enabled us to survey the whole metabolites, protein, and environmental pollutants from an organism, tissue, cell or biofluid, and decipher molecular mechanisms underlying complex human diseases. The rapid developments in this field have allowed us to generate big data at once, we would like to develop the state-of-the-art MS methods for various analysis tasks and interpreted big data with high quality.

  Environmental Chemical Exposomics and Metabolomics

  Software

  • CPVA 2.0, is coming soon.

  • CPVA Visit website

    • CPVA is a free, user-friendly tool to help users to identify peak background noises and contaminants, resulting in decrease of false-positive or redundant peak calling, thereby improving the data quality of non-targeted metabolomics studies. The CPVA used a chromatogram-centric strategy to unfold the characteristics of chromatographic peaks through visualization of peak morphology metrics, with additional functions to annotate adducts, isotopes and contaminants. (Bioinformatics. 2020,36(12):3913-3915). CPVA was deprecated. Please use CPVA 2.0 instead.

  • statTarget Visit website

    • The statTarget is a streamlined tool for quality control based signal correction, integration of metabolomics and proteomics data from multi-batch experiments, and the comprehensive statistic analysis (URL: https://stattarget.github.io). The statTarget has two basic sections. The first section is Signal Correction. It includes ‘Ensemble Learning’ for QC based signal correction. For example, QC-based random forest correction (QC-RFSC) and QC-based LOESS signal correction (QCRLSC).The second section is Statistical Analysis. It provides comprehensively computational and statistical methods that are commonly applied to analyze Omics data, and offers multiple results for biomarker discovery. The statTargetGUI provides the simple easy to use interface for the above functions, which have been online at a stand-alone app statTargetAPP.The package version is at bioconductor with statTarget.(Anal Chim Acta. 2018, 1036, 66-72.)

  • mID

• Integrating AI/ML and MS-based data from metabolomics, lipidomics, and proteomics, we would like to analyze and model complex multidimensional data, such as medical record, environmental exposure, and biological data, etc., to understand the complex biological processes of health and disease.

• Integrating AI/ML and MS-based data from metabolomics, lipidomics, and proteomics, we would like to analyze and model complex multidimensional data, such as medical record, environmental exposure, and biological data, etc., to understand the complex biological processes of health and disease.

• Discovery of active small molecule metabolites/drugs/environmental contaminants and their applications in human health/disease.

  • Rheumatoid Arthritis
  • Parkinson’s disease
  • Cancer

• PosDoc: In general, we expect that: (1) you have or will soon have a PhD degree in a related field, such as Analytical chemistry, Mass spectrometry, Bioinformatics, Computer science or statistics etc.; (2) you can program in at least one language, such as R, Java or C/C++ etc.; (3) you have published at least one first-author peer-reviewed paper in related topics. Salary

• Student: We welcome students at all levels including international students. For future PhD/Master students, you need to apply through the university program but we are happy to answer your questions by email (hm-luan[at]msn.com). For undergraduate students interested in doing an internship at our lab, please check out our research and send an email to us directly with a short introduction of yourself.

Full pulblication list in Google Scholar