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Updated: Jun 28, 2025

An Integrated Workflow of Identification and Quantification on FDR Control-Based Untargeted Metabolome

An Integrated Workflow of Identification and Quantification on FDR Control-Based Untargeted Metabolome

Published on: September 20, 2022

Exploring machine learning for untargeted metabolomics using molecular fingerprints.

Christel Sirocchi¹, Federica Biancucci², Matteo Donati¹

¹Department of Pure and Applied Sciences, University of Urbino, Piazza della Repubblica, 13, Urbino, 61029, Italy.

Computer Methods and Programs in Biomedicine

|April 16, 2024

Summary

This summary is machine-generated.

Machine learning on metabolite fingerprints helps analyze complex metabolomics data. This approach reveals new metabolic pathways and aids in understanding cellular responses beyond known biological processes.

Keywords:

Ataxia telangiectasia Machine learning Mass spectrometry Molecular fingerprinting Untargeted metabolomics

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Untargeted Metabolomics from Biological Sources Using Ultraperformance Liquid Chromatography-High Resolution Mass Spectrometry UPLC-HRMS

Untargeted Metabolomics from Biological Sources Using Ultraperformance Liquid Chromatography-High Resolution Mass Spectrometry UPLC-HRMS

Published on: May 20, 2013

Area of Science:

Biochemistry
Computational Biology
Systems Biology

Background:

Metabolomics studies cellular metabolism, offering insights into organismal states.
Analyzing large metabolomics datasets is challenging due to limited pathway annotations.

Purpose of the Study:

To apply machine learning on metabolite fingerprints for exploring metabolic processes.
To address challenges in metabolomics data analysis, including data sparsity and interpretability.

Main Methods:

Utilized machine learning on metabolite fingerprints, inspired by drug discovery techniques.
Evaluated fingerprinting effectiveness and applied feature importance analysis for interpretability.
Tested the approach on datasets related to Ataxia Telangiectasia and endothelial cells.

Main Results:

Machine learning effectively predicted metabolite responses using molecular fingerprints.
Feature importance analysis aligned with known metabolic pathways.
Identified novel metabolite groups associated with experimental conditions.

Conclusions:

The study bridges drug discovery and metabolomics by employing machine learning on metabolite fingerprints.
The approach enhances the analysis of complex metabolomics data and aids in discovering new metabolic insights.
This work provides a foundation for future research in metabolomics using advanced computational methods.