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Updated: Jul 17, 2026

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, United States.
Motivation:
Metabolomics plays an essential role in the growing systems biology approaches to unravel the relationships between metabolites and diseases. Liquid chromatography-mass spectrometry (LC-MS) is central to this effort because it can profile many metabolites from limited material. Yet, in a typical untargeted LC-MS-based metabolomics study, the majority of detected peaks remain unannotated, largely due to incomplete spectral libraries and uncertainties in peak picking, alignment, and the handling of isotopes and adducts. These limitations hinder seamless integration with other omics layers.
Results:
We developed an AI-powered platform (aiSysMet) that uses statistical, machine learning, and deep learning methods for metabolomics data processing, metabolite annotation, and integrative analysis of multi-omics data. The platform's interactive and modular web interface allows users to easily build data analysis pipelines that can be executed in the cloud.
Availability:
aiSysMet is freely available for non-commercial users on https://tools.omicscraft.com/aiSysMet.
