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Related Experiment Video

Updated: Jul 17, 2026

A Strategy for Sensitive, Large Scale Quantitative Metabolomics
14:18

A Strategy for Sensitive, Large Scale Quantitative Metabolomics

Published on: May 27, 2014

aiSysMet: AI-Powered Systems Metabolomics for Biomarker Discovery.

Habtom Ressom1, Linge Yan1, Hongyu Ao1

  • 1OmicsCraft, Washington, DC.

Bioinformatics (Oxford, England)
|July 15, 2026
PubMed
Summary

An AI-powered platform, aiSysMet, enhances metabolomics data analysis by improving metabolite annotation and multi-omics integration. This tool addresses limitations in current untargeted LC-MS studies for better disease research.

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Area of Science:

  • Systems biology
  • Metabolomics
  • Multi-omics integration

Background:

  • Metabolomics is crucial for understanding disease mechanisms and is often performed using liquid chromatography-mass spectrometry (LC-MS).
  • Untargeted LC-MS studies face challenges with unannotated metabolites due to incomplete spectral libraries and data processing issues.
  • These limitations impede the integration of metabolomics data with other omics layers.

Purpose of the Study:

  • To develop an advanced platform for processing metabolomics data, annotating metabolites, and integrating multi-omics information.
  • To overcome the limitations of current untargeted LC-MS data analysis.

Main Methods:

  • Development of an AI-powered platform (aiSysMet).
  • Utilizes statistical, machine learning, and deep learning algorithms.

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Large Scale Non-targeted Metabolomic Profiling of Serum by Ultra Performance Liquid Chromatography-Mass Spectrometry (UPLC-MS)
07:34

Large Scale Non-targeted Metabolomic Profiling of Serum by Ultra Performance Liquid Chromatography-Mass Spectrometry (UPLC-MS)

Published on: March 14, 2013

Related Experiment Videos

Last Updated: Jul 17, 2026

A Strategy for Sensitive, Large Scale Quantitative Metabolomics
14:18

A Strategy for Sensitive, Large Scale Quantitative Metabolomics

Published on: May 27, 2014

Large Scale Non-targeted Metabolomic Profiling of Serum by Ultra Performance Liquid Chromatography-Mass Spectrometry (UPLC-MS)
07:34

Large Scale Non-targeted Metabolomic Profiling of Serum by Ultra Performance Liquid Chromatography-Mass Spectrometry (UPLC-MS)

Published on: March 14, 2013

  • Features an interactive, modular web interface for cloud-based data analysis pipeline construction.

    • Main Results:

    • aiSysMet provides robust metabolomics data processing and metabolite annotation.
    • The platform facilitates the integrative analysis of multi-omics data.
    • Enables users to build and execute custom data analysis pipelines in the cloud.

    Conclusions:

    • aiSysMet offers a comprehensive solution for enhancing metabolomics data analysis and multi-omics integration.
    • The platform addresses key challenges in metabolite annotation and data processing.
    • aiSysMet is accessible for non-commercial use, promoting wider adoption in research.