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

Piecewise multivariate modelling of sequential metabolic profiling data.

Mattias Rantalainen1, Olivier Cloarec, Timothy M D Ebbels

  • 1Research Group for Chemometrics, Institute of Chemistry, Umeå University, Umeå, S-901 87, Sweden. mattias.rantalainen@imperial.ac.uk

BMC Bioinformatics
|February 21, 2008
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

Metabolic pathways associated with cardiometabolic risk effects on cognition in middle-aged adults: the CARDIA study.

Metabolomics : Official journal of the Metabolomic Society·2026
Same author

Sex-based differences in long-term lipid metabolism, inflammation, and stress regulation after non-severe paediatric burns.

Burns : journal of the International Society for Burn Injuries·2026
Same author

Metabolism and Excretion of Synthetic Extended Viperin Pathway Deoxydidehydronucleosides in the Sprague-Dawley Rat.

Journal of proteome research·2026
Same author

Methoxyacetic acid exposure in rats induces N-butyrylglycinuria consistent with beta-oxidation impairment.

Archives of toxicology·2026
Same author

Disordered Bile Acid Metabolism in Alcohol-Related Hepatitis.

Alimentary pharmacology & therapeutics·2026
Same author

Corrigendum to "Unravelling inulin molecules in food sources using a matrix-assisted laser desorption/ionization magnetic resonance mass spectrometry (MALDI-MRMS) pipeline" [Food Res. Int. 184 (2024) 114276].

Food research international (Ottawa, Ont.)·2026
Same journal

OpenIMC: an open-source platform for analyzing single-cell and spatial proteomics by imaging mass cytometry.

BMC bioinformatics·2026
Same journal

NAP: an open source pipeline for cross-domain microbiome profiling using Nanopore sequencing-derived amplicon data.

BMC bioinformatics·2026
Same journal

SurvGME: an R package for survival analysis with graphical and measurement error models.

BMC bioinformatics·2026
Same journal

SimMapNet: a Bayesian framework for gene regulatory network inference using gene ontology similarities as external hint.

BMC bioinformatics·2026
Same journal

Dual channel drug-drug interactions extraction based on cross attention.

BMC bioinformatics·2026
Same journal

FeSseqdb: a curated sequence-level database and interpretable machine learning framework for identifying iron-sulfur proteins.

BMC bioinformatics·2026
See all related articles

This study introduces a new method for analyzing biological data with limited time points. It effectively models and predicts dynamic changes in short, sparse time-series data, offering clear insights.

Area of Science:

  • Biotechnology
  • Systems Biology
  • Metabolomics

Background:

  • Accurate modeling of biological systems' time-related behavior is crucial for understanding dynamic responses.
  • Metabolic profiling studies often face limitations in sampling rate and number of data points.

Purpose of the Study:

  • To develop a supervised multivariate modeling approach for time-related variations in short, sparsely sampled time-series data.
  • To enable accurate modeling and prediction of dynamic changes in biological systems under experimental constraints.

Main Methods:

  • Utilized a series of piecewise Orthogonal Projections to Latent Structures (OPLS) models.
  • Combined linear OPLS models to accommodate non-linear time-course changes.
  • Applied the method to both simulated and real metabolic profiling datasets.

Related Experiment Videos

Main Results:

  • Successfully modeled and predicted time-related variations in short, sparse multivariate data.
  • Demonstrated the ability to capture non-linear dynamics through piecewise model combinations.
  • Validated the approach on simulated and experimental metabolic profiling data.

Conclusions:

  • The proposed method is effective for modeling and predicting short, multivariate time-series data.
  • Offers model transparency for easy interpretation of time-related variations.
  • Complements existing methods like OPLS and Principal Component Analysis (PCA) for time-series analysis.