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

Two-Way ANOVA

Two-Way ANOVA

The two-way ANOVA is an extension of the one-way ANOVA. It is a statistical test performed on three or more samples categorized by two factors - a row factor and a column factor. Ronald Fischer mentioned it in 1925 in his book 'Statistical Methods for Researchers.'
The two-way ANOVA analysis initially begins by stating the null hypothesis that there is an interaction effect between the two factors of a dataset. This effect can be visualized using line segments formed by joining the...

Factorial Design

Factorial Design

Factorial Analysis is an experimental design that applies Analysis of Variance (ANOVA) statistical procedures to examine a change in a dependent variable due to more than one independent variable, also known as factors. Changes in worker productivity can be reasoned, for example, to be influenced by salary and other conditions, such as skill level. One way to test this hypothesis is by categorizing salary into three levels (low, moderate, and high) and skills sets into two levels (entry level...

Expected Frequencies in Goodness-of-Fit Tests

Expected Frequencies in Goodness-of-Fit Tests

A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n) to the number of categories (k).

One-Way ANOVA: Unequal Sample Sizes

One-Way ANOVA: Unequal Sample Sizes

One-way ANOVA can be performed on three or more samples of unequal sizes. However, calculations get complicated when sample sizes are not always the same. So, while performing ANOVA with unequal samples size, the following equation is used:

Frequency-dependent Selection

Frequency-dependent Selection

When the fitness of a trait is influenced by how common it is (i.e., its frequency) relative to different traits within a population, this is referred to as frequency-dependent selection. Frequency-dependent selection may occur between species or within a single species. This type of selection can either be positive—with more common phenotypes having higher fitness—or negative, with rarer phenotypes conferring increased fitness.

Dose-Response Relationship: Selectivity and Specificity

Dose-Response Relationship: Selectivity and Specificity

Drugs exert their therapeutic effects by interacting with receptors, enzymes, or ion channels that are present throughout the human body. The strength and duration of the interaction between a drug and its target receptor are characterized by the selectivity and specificity of the drug. Selectivity refers to a drug's strong preference for its intended target over other targets. For instance, isoprenaline, a non-selective β-adrenergic agonist, interacts with both β1- and...

You might also read

Related Articles

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

Sort by

Same author

Clinical trials and evaluation of AI tools in solid organ transplantation: implications for clinical care, regulatory science, and rare diseases.

NPJ digital medicine·2026

Same author

Genetically proxied inhibition of cholesterol-lowering drug targets and survival in HPV-positive and non-HPV driven head and neck cancer: a multicentre MR study.

Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology·2026

Same author

Identification of a Cytokine Biomarker for Prognostic Modeling of Breast Cancer-Related Lymphedema.

Cancer research communications·2026

Same author

Genome-wide association study of early-stage non-small cell lung cancer prognosis: a pooled analysis in the International Lung Cancer Consortium.

Carcinogenesis·2025

Same author

Mediation CNN (Med-CNN) Model for High-Dimensional Mediation Data.

International journal of molecular sciences·2025

Same author

Clinical integration of germline findings from a tumor testing precision medicine program.

BMC cancer·2025

Search research articles

Related Experiment Video

Updated: Nov 17, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

HDSI: High dimensional selection with interactions algorithm on feature selection and testing.

Rahi Jain¹, Wei Xu^1,2

¹Biostatistics Department, Princess Margaret Cancer Research Centre, Toronto, Ontario, Canada.

|February 16, 2021

Summary

This summary is machine-generated.

A new High Dimensional Selection with Interactions (HDSI) algorithm effectively selects significant features from high-dimensional data, including interaction effects, outperforming existing methods like LASSO.

More Related Videos

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Related Experiment Videos

Last Updated: Nov 17, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Area of Science:

Statistical Learning
Computational Statistics
Bioinformatics

Background:

High-dimensional data presents challenges for classical statistical learning.
Existing methods like Random LASSO struggle with interaction terms and statistical significance testing.

Purpose of the Study:

To introduce the High Dimensional Selection with Interactions (HDSI) algorithm.
To address limitations in feature selection for high-dimensional data with interactions.

Main Methods:

HDSI applies statistical techniques (e.g., LASSO) to bootstrapped samples with random features and interactions.
It pools selected features and determines statistical significance for final selection.
Final coefficients are estimated using appropriate statistical techniques.

Main Results:

HDSI successfully handles high-dimensional data and incorporates interaction terms.
The algorithm provides statistical inferences for selected features.
HDSI demonstrated superior performance compared to LASSO, subset selection, adaptive LASSO, random LASSO, and group LASSO in simulations and real studies.

Conclusions:

HDSI is a robust feature selection method for high-dimensional data with interactions.
It offers improved statistical rigor and performance over existing algorithms.
The method enhances the application of classical statistical techniques in complex datasets.