Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Threshold extraction in metabolite concentration data.

A Flöter1, J Nicolas, T Schaub

  • 1University of Potsdam, Institute for Computer Science, August-Bebel-Str. 89/Hs. 4, 14482 Potsdam, Germany. floeter@cs.uni-postdam.de

Bioinformatics (Oxford, England)
|July 3, 2004
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Glial reactivity and cognitive decline follow chronic heterochromatin loss in neurons.

Nature communications·2025
Same author

Feasibility of organic micropollutant removal from municipal wastewaters by algal treatment vs. activated sludge treatment: Comparison based on non-targeted organic compound analysis.

Journal of environmental management·2024
Same author

The ICD-11 developmental field study of reliability of diagnoses of high-burden mental disorders: results among adult patients in mental health settings of 13 countries.

World psychiatry : official journal of the World Psychiatric Association (WPA)·2018
Same author

Ceftolozane/tazobactam in the treatment of osteomyelitis and skin and soft-tissue infections due to extensively drug-resistant Pseudomonas aeruginosa: clinical and microbiological outcomes.

International journal of antimicrobial agents·2017
Same author

Direct sample introduction-gas chromatography-mass spectrometry for the determination of haloanisole compounds in cork stoppers.

Journal of chromatography. A·2016
Same author

Boolean network identification from perturbation time series data combining dynamics abstraction and logic programming.

Bio Systems·2016
Same journal

3DICE: Interpretable 3D Cross-Modal Learning for Drug-Target Interaction Prediction and Large-Scale Drug Discovery.

Bioinformatics (Oxford, England)·2026
Same journal

KASSPer: Kinase Active Site Structure Prediction using Protein and Ligand Language Models and Its Application to Virtual Screening.

Bioinformatics (Oxford, England)·2026
Same journal

IDR searcher: a search engine solution for public image resources.

Bioinformatics (Oxford, England)·2026
Same journal

KCFtools: Rapid alignment-free method for introgression screening and GWAS using k-mer profiles.

Bioinformatics (Oxford, England)·2026
Same journal

Meta2DB: Curated shotgun metagenomic feature sets and metadata for health state prediction.

Bioinformatics (Oxford, England)·2026
Same journal

conMItion: an R package adjusting confounding factors for associations in multi-omics.

Bioinformatics (Oxford, England)·2026
See all related articles

This study introduces a novel decision tree method to identify stable metabolite concentration states. The approach robustly detects thresholds, revealing known and potentially new biological states from complex data.

Area of Science:

  • Metabolomics
  • Systems Biology
  • Bioinformatics

Background:

  • Advancements in analytical techniques like gas chromatography-mass spectrometry generate large metabolite concentration datasets.
  • Understanding metabolite dynamics requires identifying stable states characterized by simple concentration behavior.
  • Existing methods for finding discretization thresholds are insufficient for detecting states in concentration data due to weak conditional dependencies.

Purpose of the Study:

  • To develop a robust method for recognizing stable states in metabolite concentration data.
  • To overcome limitations of general discretization techniques in identifying biologically relevant states.

Main Methods:

  • Utilized decision tree induction for state recognition.
  • Employed global analysis of decision forests, evaluating stability and quality.

Related Experiment Videos

  • Developed a method for detecting comprehensible and robust thresholds.

    • Main Results:

    • Successfully applied the method to metabolite concentration data.
    • Discovered hidden states within the concentration variables.
    • Identified states that align with known biological experimental properties.
    • Uncovered putative new biological states.

    Conclusions:

    • The decision tree-based approach provides a powerful tool for analyzing complex metabolomic data.
    • This method enhances the discovery of biological states, contributing to a deeper understanding of metabolite dynamics.
    • The discovered states offer insights into both established and novel biological phenomena.