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

Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches

Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches

Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...

You might also read

Related Articles

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

Sort by

Same author

A lipidomics roadmap: from basic research to societal challenges.

Nature communications·2026

Same author

Lipid Class Prediction from MS1 Data using Gaussian Graphical Models.

Analytical chemistry·2026

Same author

Optimisation and application of the metabolite extraction method of vitreous humour samples.

Journal of chromatography. A·2026

Same author

Decoding the Oxylipin Chemical Space Using Ion Identity Molecular Networking.

Analytical chemistry·2026

Same author

Early metabolic alterations in pediatric obesity: Depot-specific insights from untargeted adipose tissue metabolomics.

Journal of clinical lipidology·2026

Same author

Weaponizing nutrition: plants use a double strategy to fight herbivory, converting nutritionally essential fatty acids into defensive oxylipin signals.

The New phytologist·2026

Same journal

Kinship Inferences for Second-Degree Relatives With a Combination of STRs and Microhaplotypes.

Electrophoresis·2026

Same journal

Optimisation of Electrokinetic Extraction System: Colourimetric Determination of Copper (II) in Sand Using Polymer Inclusion Membrane.

Electrophoresis·2026

Same journal

Novel Phloroglucinol Derivatives as Neuraminidase Inhibitors Identified From Humulus lupulus L. Extract by At-Line Nanofractionation Platform.

Electrophoresis·2026

Same journal

Protein-Based High-Performance Liquid Chromatography and Cyclodextrin-Capillary Electrokinetic Chromatography for the Chiral Separation of Azoles.

Electrophoresis·2026

Same journal

Dynamics of Heparin Translocations Through Solid-State Nanopores.

Electrophoresis·2026

Same journal

Production of Protein Hydrolysates and Bioactive Peptides From Lablab purpureus and Macrotyloma uniflorum via Optimized Extraction and Proteolysis Protocols.

Electrophoresis·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Apr 3, 2026

A Strategy for Sensitive, Large Scale Quantitative Metabolomics

A Strategy for Sensitive, Large Scale Quantitative Metabolomics

Published on: May 27, 2014

Missing value imputation strategies for metabolomics data.

Emily Grace Armitage¹, Joanna Godzien¹, Vanesa Alonso-Herranz¹

¹Centre for Metabolomics and Bioanalysis (CEMBIO), Facultad de Farmacia, Universidad CEU San Pablo, Madrid, Spain.

Electrophoresis

|September 17, 2015

Summary

This summary is machine-generated.

Understanding missing data is crucial. K-means nearest neighbor imputation best restores data normality and variance, while Bonferroni correction effectively manages statistical significance, even with up to 40% missing values.

Keywords:

CE-MS Data False-discovery rate Imputation Metabolomics Missing values k-nearest neighbour

More Related Videos

Untargeted Liquid Chromatography-Mass Spectrometry-Based Metabolomics Analysis of Wheat Grain

Untargeted Liquid Chromatography-Mass Spectrometry-Based Metabolomics Analysis of Wheat Grain

Published on: March 13, 2020

Multi-step Preparation Technique to Recover Multiple Metabolite Compound Classes for In-depth and Informative Metabolomic Analysis

Multi-step Preparation Technique to Recover Multiple Metabolite Compound Classes for In-depth and Informative Metabolomic Analysis

Published on: July 11, 2014

Related Experiment Videos

Last Updated: Apr 3, 2026

A Strategy for Sensitive, Large Scale Quantitative Metabolomics

A Strategy for Sensitive, Large Scale Quantitative Metabolomics

Published on: May 27, 2014

Untargeted Liquid Chromatography-Mass Spectrometry-Based Metabolomics Analysis of Wheat Grain

Untargeted Liquid Chromatography-Mass Spectrometry-Based Metabolomics Analysis of Wheat Grain

Published on: March 13, 2020

Multi-step Preparation Technique to Recover Multiple Metabolite Compound Classes for In-depth and Informative Metabolomic Analysis

Multi-step Preparation Technique to Recover Multiple Metabolite Compound Classes for In-depth and Informative Metabolomic Analysis

Published on: July 11, 2014

Area of Science:

Data Science
Statistical Analysis
Bioinformatics

Background:

Missing data significantly impacts data normality, variance, and statistical significance.
Different origins of missing values necessitate varied handling strategies.
Effective imputation and error rate control are vital for reliable data analysis.

Purpose of the Study:

To compare four imputation methods regarding their effects on data normality, variance, and statistical significance.
To evaluate strategies for controlling familywise error rate and false discovery in conjunction with imputation methods.
To determine a suitable threshold for accepting missing data as truly missing.

Main Methods:

Comparative analysis of four missing value imputation techniques.
Evaluation of familywise error rate and false discovery control strategies.
Assessment of imputation effects on data normality, variance, and statistical significance.

Main Results:

K-means nearest neighbor imputation demonstrated superior performance in restoring data normality and variance.
Bonferroni correction proved most effective in maximizing true positives and minimizing false positives.
As much as 40% of missing data can be considered truly missing, with a "gray area" identified between 40-70%.

Conclusions:

K-means nearest neighbor imputation is recommended for restoring data characteristics affected by missing values.
Bonferroni correction is optimal for managing statistical significance and error rates.
A proposed strategy balances imputation with accurate identification of truly missing data, especially within the 40-70% missing range.