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 Concept Videos

Associative Learning01:27

Associative Learning

1.2K
Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
1.2K
Association Areas of the Cortex01:21

Association Areas of the Cortex

8.8K
Association areas are regions of the cerebral cortex that do not have a specific sensory or motor function. Instead, they integrate and interpret information from various sources to enable higher cognitive processes such as memory, learning, and decision-making. Some key association areas include the following:
Prefrontal Association Area: This area is located in the frontal lobe and is involved in planning, decision-making, and moderating social behavior. It connects with primary motor areas,...
8.8K
Cluster Sampling Method01:20

Cluster Sampling Method

14.0K
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...
14.0K
Measures of Central Tendency02:16

Measures of Central Tendency

20.2K
The "center" of a data set is also a way of describing location. The two most widely used measures of the "center" of the data are the mean (average) and the median. The words "mean" and "average" are often used interchangeably. The substitution of one word for the other is common practice. The technical term is "arithmetic mean" and "average" is technically a center location. However, in practice among non-statisticians,...
20.2K
Protein Networks02:26

Protein Networks

4.5K
An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
4.5K
Central Tendency: Analysis01:10

Central Tendency: Analysis

468
Measures of central tendency are tools used in biostatistics to identify the average or center of a dataset. They offer a single representative value for understanding and summarizing data distribution.
The mean is one such measure, calculated by totaling all values in a dataset and dividing by the number of values. For instance, the mean blood pressure reading (120, 130, 140, 150) would be 135. However, the mean can be affected by extreme values or outliers.
The median, another measure,...
468

You might also read

Related Articles

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

Sort by
Same author

Household environmental characteristics influence the house dust metagenome.

Environmental research·2026
Same author

Prospective Associations of Breastfeeding Parents' Postpartum Dietary Intake With Infant Gut Microbiome at Age 6 Months in the Pregnancy Eating Attributes Study.

Journal of the Academy of Nutrition and Dietetics·2025
Same author

Statistical analysis of correlated expression data from high throughput experiments.

Genetics·2025
Same author

Oral microbiota related to allergy in Norwegian adults.

The journal of allergy and clinical immunology. Global·2025
Same author

Dietary Sugar and Saturated Fat Consumption Associated with the Gastrointestinal Microbiome during Pregnancy.

The Journal of nutrition·2024
Same author

Calorie restriction outperforms bariatric surgery in a murine model of obesity and triple-negative breast cancer.

JCI insight·2023
Same journal

Regression Trees and Ensemble for Multivariate Outcomes.

Sankhya. Series B. [Methodological.]·2025
Same journal

Word Embeddings as Statistical Estimators.

Sankhya. Series B. [Methodological.]·2025
Same journal

Mediation Analysis using Semi-parametric Shape-Restricted Regression with Applications.

Sankhya. Series B. [Methodological.]·2024
Same journal

A Blockwise Consistency Method for Parameter Estimation of Complex Models.

Sankhya. Series B. [Methodological.]·2021
Same journal

Local linear estimation for spatial random processes with stochastic trend and stationary noise.

Sankhya. Series B. [Methodological.]·2019
Same journal

NONPARAMETRIC BENCHMARK ANALYSIS IN RISK ASSESSMENT: A COMPARATIVE STUDY BY SIMULATION AND DATA ANALYSIS.

Sankhya. Series B. [Methodological.]·2013
See all related articles

Related Experiment Video

Updated: Jan 14, 2026

Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications
09:20

Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications

Published on: February 23, 2019

9.2K

Cluster Based Association Measures with Applications.

Sabyasachi Bera1, Farnaz Fouladi1, Shyamal Peddada1

  • 1Biostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, 111 T.W. Alexander Dr., Durham, 27709, North Carolina, USA.

Sankhya. Series B. [Methodological.]
|October 20, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces CLuster based Association Measures (CLAM), a novel method to quantify variable associations in complex datasets. CLAM effectively identifies hidden clusters and arbitrary relationships, overcoming limitations of traditional correlation methods.

More Related Videos

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

16.1K
Measuring the Functional Abilities of Children Aged 3-6 Years Old with Observational Methods and Computer Tools
11:29

Measuring the Functional Abilities of Children Aged 3-6 Years Old with Observational Methods and Computer Tools

Published on: June 20, 2020

9.7K

Related Experiment Videos

Last Updated: Jan 14, 2026

Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications
09:20

Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications

Published on: February 23, 2019

9.2K
Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

16.1K
Measuring the Functional Abilities of Children Aged 3-6 Years Old with Observational Methods and Computer Tools
11:29

Measuring the Functional Abilities of Children Aged 3-6 Years Old with Observational Methods and Computer Tools

Published on: June 20, 2020

9.7K

Area of Science:

  • Biostatistics
  • Bioinformatics
  • Data Science

Background:

  • Variable relationships are often non-linear and datasets may contain hidden subgroups.
  • Standard correlation measures like Pearson or Spearman can be misleading in such complex scenarios.
  • High-dimensional data with substructures are increasingly common in biomedical research.

Purpose of the Study:

  • To develop a novel association procedure that accounts for hidden data clusters.
  • To quantify associations between univariate and multivariate variables, irrespective of their relationship form.
  • To provide a robust measure for heterogeneous data common in biomedical research.

Main Methods:

  • Developed CLuster based Association Measures (CLAM), a novel procedure.
  • Integrated clustering algorithms to detect hidden subgroups.
  • Utilized association measures suitable for arbitrary relationships within detected clusters.

Main Results:

  • CLAM accurately quantifies associations in data with hidden clusters.
  • The method is versatile, applicable to both univariate and multivariate variables.
  • Demonstrated performance on synthetic and diverse real-world datasets including cell-cycle genes, microbiome data, and imaging datasets.

Conclusions:

  • CLAM offers a robust solution for association analysis in complex, heterogeneous datasets.
  • The method addresses the limitations of traditional correlation measures in the presence of hidden substructures.
  • CLAM is well-suited for biomedical research and other fields generating high-dimensional data.