Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...

Uncertainty: Overview

Uncertainty: Overview

In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.

Propagation of Uncertainty from Systematic Error

Propagation of Uncertainty from Systematic Error

The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...

You might also read

Related Articles

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

Sort by

Same author

Study on the Synergistic Mechanism of Zwitterionic Surfactants and Nano-SiO<sub>2</sub> Particles in Oil Displacement.

Langmuir : the ACS journal of surfaces and colloids·2026

Same author

Ion-triggered reconfigurable hydrogels with salt-enhanced mechanical and swelling properties via network topological adaptation.

Nature communications·2026

Same author

Late holocene overflow floods of the lower Yellow River led to the shrinkage of downstream lakes.

Scientific reports·2025

Same author

The role and mechanism of bacterial outer membrane vesicles in the development of periodontitis.

Frontiers in microbiology·2025

Same author

The Interfacial Dilational Rheology of Surfactant Solutions with Low Interfacial Tension.

Molecules (Basel, Switzerland)·2025

Same author

BCI-Utility Metric for Asynchronous P300 Brain-Computer Interface Systems.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society·2023

Search research articles

Related Experiment Video

Updated: Jun 29, 2025

A Strategy for Sensitive, Large Scale Quantitative Metabolomics

A Strategy for Sensitive, Large Scale Quantitative Metabolomics

Published on: May 27, 2014

Bayesian functional analysis for untargeted metabolomics data with matching uncertainty and small sample sizes.

Guoxuan Ma¹, Jian Kang¹, Tianwei Yu²

¹Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA.

Briefings in Bioinformatics

|April 6, 2024

Summary

Bayesian Analysis of Untargeted Metabolomics data (BAUM) addresses noise and uncertainty in metabolomics data. This novel approach improves metabolite identification and functional analysis, even with small sample sizes.

Keywords:

Bayesian latent factor model matching uncertainty metabolite network analysis

More Related Videos

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

Published on: November 10, 2023

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

Related Experiment Videos

Last Updated: Jun 29, 2025

A Strategy for Sensitive, Large Scale Quantitative Metabolomics

A Strategy for Sensitive, Large Scale Quantitative Metabolomics

Published on: May 27, 2014

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

Published on: November 10, 2023

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

Area of Science:

Metabolomics
Biochemistry
Computational Biology

Background:

Untargeted metabolomics using liquid chromatography-mass spectrometry (LC-MS) is widely used to study global metabolic patterns.
Data generated from these studies are often noisy, with ambiguous metabolite identities and multiple potential matches for detected features.
This uncertainty significantly hinders downstream functional analysis and accurate biological interpretation.

Purpose of the Study:

To develop a novel computational approach for robust analysis of untargeted metabolomics data.
To integrate metabolite identification, selection, and functional analysis into a unified framework.
To address challenges of data noise, feature-metabolite matching uncertainty, and small sample sizes.

Main Methods:

Developed Bayesian Analysis of Untargeted Metabolomics data (BAUM), a novel computational framework.
Integrated knowledge graphs of variable relationships to improve analysis.
Incorporated Bayesian inference to handle matching uncertainty and assign confidence levels to feature-metabolite matches.
Applied the method to datasets with small sample sizes and partially known feature identities.

Main Results:

BAUM demonstrated superior accuracy in selecting functionally consistent metabolites compared to existing methods.
The approach effectively assigns confidence scores to feature-metabolite matches, reducing ambiguity.
Analysis of COVID-19 and mouse brain metabolomics datasets showed BAUM to be robust and stable, even with small sample sizes (n=16).

Conclusions:

BAUM provides a powerful and integrated solution for untargeted metabolomics data analysis.
The method enhances metabolite identification accuracy and facilitates reliable functional pathway analysis.
BAUM is applicable to diverse metabolomics datasets, including those with limited sample sizes, uncovering both known and novel biological insights.