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

Relative Frequency Histogram01:14

Relative Frequency Histogram

5.6K
The relative frequency depicts the proportion of data points that have each value. The frequency tells the number of data points that have each value. Like the histogram, a relative frequency histogram also has the same shape with a horizontal scale (the x-axis), but the vertical scale (the y-axis) is marked with relative frequencies (percentages of the whole) instead of actual frequencies. A relative frequency histogram is a graphical representation of a frequency distribution where the...
5.6K
Percentage Frequency Distribution00:57

Percentage Frequency Distribution

58.9K
A percentage frequency distribution, in general, is a display of data that indicates the percentage of observations for each data point or grouping of data points. It is a commonly used method for expressing the relative frequency of survey responses and other data. The percentage frequency distributions are often displayed as bar graphs, pie charts, or tables.
The process of making a percentage frequency distribution involves the following few steps: note the total number of observations;...
58.9K
Fixed Action Patterns01:06

Fixed Action Patterns

16.3K
A fixed action pattern (FAP) is a specific, hard-wired sequence of behaviors that occurs in response to an external stimulus, called a sign stimulus. The behavior is “fixed” because it is essentially unchangeable—proceeding similarly across individuals of a species every time it occurs.
16.3K
Determination of Expected Frequency01:08

Determination of Expected Frequency

2.2K
Suppose one wants to test independence between the two variables of a contingency table. The values in the table constitute the observed frequencies of the dataset. But how does one determine the expected frequency of the dataset? One of the important assumptions is that the two variables are independent, which means the variables do not influence each other. For independent variables, the statistical probability of any event involving both variables is calculated by multiplying the individual...
2.2K
Mass Analyzers: Common Types01:19

Mass Analyzers: Common Types

674
The quadrupole mass analyzer consists of four cylindrical metal rods arranged in a diamond carrying a DC voltage and a radio-frequency AC voltage. The motion of ions through the quadrupole depends on the field strength, causing only ions of a certain m/z to resonate successfully and strike the detector at a given field strength. Though the transmission rate for these analyzers is high, the exact elemental composition of the sample is not determined because of low resolution; however, they are...
674
Scatter Plot01:15

Scatter Plot

7.1K
The most common and easiest way to display the relationship between two variables, x and y, is a scatter plot. A scatter plot shows the direction of a relationship between the variables. A clear direction happens when there is either:
7.1K

You might also read

Related Articles

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

Sort by
Same author

Sex-Specific Polygenic Risk Scores and Replication in a Model-Free Analysis of Schizophrenia Data.

Genes·2025
Same author

Digenic Analysis Finds Highly Interactive Genetic Variants Underlying Polygenic Traits.

Medical research archives·2024
Same author

A multi-threaded approach to genotype pattern mining for detecting digenic disease genes.

Frontiers in genetics·2023
Same author

A statistical look at the COVID-19 vaccine development and vaccine policies.

Frontiers in public health·2022
Same author

Machine learning models to predict the maximum severity of COVID-19 based on initial hospitalization record.

Frontiers in public health·2022
Same author

Combining <i>p</i>-values from various statistical methods for microbiome data.

Frontiers in microbiology·2022
Same journal

Precision Medicine Gene Network Analyser: part I-cancer driver gene identification through network topology and ensemble machine learning.

Genomics & informatics·2026
Same journal

A bioinformatics pipeline for the design of a SART3-targeted cancer vaccine with enhanced immunogenicity.

Genomics & informatics·2026
Same journal

Measuring the gap: correlating synthetic-to-real drift with PHI de-identification performance.

Genomics & informatics·2026
Same journal

Correction: Towards a transparent and reproducible AI-assisted research paper writing.

Genomics & informatics·2026
Same journal

Correction: Peptide‑based therapeutics targeting the SLC39A14‑PIWIL2 fusion in hepatocellular carcinoma.

Genomics & informatics·2026
Same journal

BioOne: a national-scale platform for integrated discovery and utilization of diverse biological resources in South Korea.

Genomics & informatics·2026
See all related articles

Related Experiment Video

Updated: Aug 14, 2025

Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

20.0K

Overview of frequent pattern mining.

Jurg Ott1, Taesung Park2

  • 1Laboratory of Statistical Genetics, Rockefeller University, New York, NY 10065, USA.

Genomics & Informatics
|January 8, 2023
PubMed
Summary
This summary is machine-generated.

Frequent pattern mining identifies genotype patterns linked to disease. Statistical assessment of these disease-associated diplotypes presents unique challenges, requiring careful evaluation in genetic studies.

Keywords:
data mininggenotype patternmachine learningpattern recognitionstatistical significance

More Related Videos

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

8.8K
Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
07:41

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

Published on: May 17, 2019

9.0K

Related Experiment Videos

Last Updated: Aug 14, 2025

Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

20.0K
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

8.8K
Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
07:41

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

Published on: May 17, 2019

9.0K

Area of Science:

  • Genetics
  • Bioinformatics
  • Computational Biology

Background:

  • Frequent pattern mining is utilized in genetic research.
  • Focus on identifying associations between genotype patterns (diplotypes) and disease.
  • Diplotypes involve combined genotypes at different DNA variants.

Purpose of the Study:

  • To apply frequent pattern mining to genetic data for disease association studies.
  • To identify genotype patterns more prevalent in affected individuals.
  • To outline the unique statistical challenges in assessing significance for these patterns.

Main Methods:

  • Application of frequent pattern mining algorithms.
  • Analysis of genotype data from affected and unaffected individuals.
  • Exploration of methods for assessing statistical significance of identified patterns.

Main Results:

  • Identification of specific genotype patterns (diplotypes) associated with disease.
  • Highlighting the complexities in determining the statistical significance of these findings.
  • Demonstration of the utility of frequent pattern mining in genetic epidemiology.

Conclusions:

  • Frequent pattern mining is a valuable tool for discovering disease-associated genotype patterns.
  • The statistical assessment of significance for identified diplotypes requires specialized approaches.
  • Further research is needed to refine statistical methods for these genetic association studies.