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

Cluster Sampling Method

Cluster Sampling Method

Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...

Time-Series Graph

Time-Series Graph

A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...

Properties of Laplace Transform-II

Properties of Laplace Transform-II

Time differentiation, convolution, integration, and periodicity are fundamental concepts in analyzing functions and signals over time. Each concept provides a unique perspective on how functions evolve, interact, and repeat, offering essential tools for various scientific and engineering applications.
Time differentiation involves analyzing the rate of change of a function over time. Mathematically, it is the derivative of a function with respect to time. This concept can be likened to tracking...

Drug Concentration Versus Time Correlation

Drug Concentration Versus Time Correlation

The plasma drug concentration-time curve is a crucial tool in pharmacokinetics, representing the drug's concentration in plasma at different time intervals post-administration. This curve illustrates the drug's journey from absorption into the systemic circulation, distribution to body tissues, and eventual elimination through excretion or biotransformation.
Two pivotal parameters are the minimum effective concentration (MEC) and the minimum toxic concentration (MTC). The MEC is the lowest drug...

Periodic Classification of the Elements

Periodic Classification of the Elements

The periodic table arranges atoms based on increasing atomic number so that elements with the same chemical properties recur periodically. When their electron configurations are added to the table, a periodic recurrence of similar electron configurations in the outer shells of these elements is observed. Because they are in the outer shells of an atom, valence electrons play the most important role in chemical reactions. The outer electrons have the highest energy of the electrons in an atom...

Noncompartmental Analysis: Mean Residence Time

Noncompartmental Analysis: Mean Residence Time

According to statistical moment theory, mean residence time (MRT) is an important measure in pharmacokinetics. MRT can be defined as the expected mean of a probability density function distribution. It provides valuable insights into drug disposition in the body.
After the administration of a drug through intravenous bolus injection, the drug molecules are distributed throughout the body and remain there for varying periods. The MRT represents the average time these drug molecules stay in the...

You might also read

Related Articles

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

Sort by

Same author

Model-based clustering of multiple images incorporating covariates.

Statistical methods in medical research·2026

Same author

Incidence of hidradenitis suppurativa in transgender and cisgender individuals: A multicenter matched cohort study.

Journal of the American Academy of Dermatology·2026

Same author

Acne Incidence and Severity in Transgender Individuals.

JAMA dermatology·2026

Same author

The 2018-2019 FoodNet Population Survey: a tool to estimate risks and behaviors associated with enteric infections.

American journal of epidemiology·2024

Same author

Assessing intra- and inter-method agreement of functional data.

Statistical methods in medical research·2023

Same author

Evaluation of SaTo pans as a new latrine technology in Kisumu County healthcare facilities, Kenya.

Tropical medicine & international health : TM & IH·2023

Search research articles

Related Experiment Video

Updated: May 10, 2026

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

Published on: January 16, 2019

Clustering Based on Periodicity in High-Throughput Time Course Data.

Anna J Blackstock¹, Amita K Manatunga, Youngja Park

¹Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA, USA.

Statistical Analysis and Data Mining

|June 14, 2013

Summary

This summary is machine-generated.

This study introduces a new method for clustering Nuclear Magnetic Resonance (NMR) peaks based on their periodic behavior, improving the analysis of time-varying metabolite levels in biological samples.

Related Experiment Videos

Last Updated: May 10, 2026

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

Published on: January 16, 2019

Area of Science:

Biochemistry
Analytical Chemistry
Bioinformatics

Background:

Nuclear Magnetic Resonance (NMR) spectroscopy is increasingly used for metabolite profiling in biological samples.
Metabolite concentrations fluctuate over time, necessitating methods to analyze dynamic changes.
Extracting NMR peaks with periodic behavior is crucial for understanding biological rhythms.

Purpose of the Study:

To develop a novel method for clustering NMR peaks exhibiting periodic behavior.
To improve the analysis of time-series metabolomics data.
To account for parameter estimation variability in clustering.

Main Methods:

Clustering of NMR peaks based on periodic behavior using periodic regression.
Employing a mixture model to group peaks, considering the variability of period estimates.
Application to human blood plasma NMR data collected over a 24-hour period.

Main Results:

The proposed method effectively clusters NMR peaks with periodic patterns.
Simulation studies confirmed the importance of accounting for variance in parameter estimates during clustering.
The approach is applicable to real-world biological time-series data.

Conclusions:

The developed method enhances the analysis of dynamic metabolite changes using NMR spectroscopy.
Accurate clustering of periodic NMR peaks requires consideration of estimation variability.
This technique offers a valuable tool for time-resolved metabolomics research.