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

Multiple Comparison Tests

Multiple Comparison Tests

Multiple comparison test, abbreviated as MCT, is a post hoc analysis generally performed after comparing multiple samples with one or more tests. An MCT will help identify a significantly different sample among multiple samples or a factor among multiple factors.
It would be easy to compare two samples using a significance alpha level of 0.05. In other words, there is only one sample pair to be compared. However, it would be difficult to identify a significantly different sample if the number...

DNA Microarrays

DNA Microarrays

Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...

Comparing the Survival Analysis of Two or More Groups

Comparing the Survival Analysis of Two or More Groups

Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and Cox...

Evolutionary Relationships through Genome Comparisons

Evolutionary Relationships through Genome Comparisons

Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...

Comparing Copy Number Variations and SNPs

Comparing Copy Number Variations and SNPs

Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
Copy number variations or CNVs are the structural variations that cover more than 1kb of DNA sequence. The single nucleotide polymorphism (SNP), on the other hand, is a single nucleotide change or a point mutation that is found in more than 1%...

You might also read

Related Articles

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

Sort by

Same author

Automatic assessment of lung involvement in systemic sclerosis using deep learning.

Journal of research in medical sciences : the official journal of Isfahan University of Medical Sciences·2026

Same author

Objective Evaluation of Small Bowel Visualization Quality in Wireless Capsule Endoscopy Images Using Generative Adversarial Network.

Advanced biomedical research·2025

Same author

Lessons Learned: Quality Analysis of Optical Coherence Tomography in Neuromyelitis Optica.

Annals of clinical and translational neurology·2025

Same author

Comorbidities Are Associated With Unfavorable Outcome in Aquaporin-4 Antibody Positive Neuromyelitis Optica Spectrum Disorders and Myelin Oligodendrocyte Glycoprotein Antibody-Associated Disease: Exploratory Study From the CROCTINO Cohort.

European journal of neurology·2025

Same author

Multivariate Gaussian Bayes classifier with limited data for segmentation of clean and contaminated regions in the small bowel capsule endoscopy images.

PloS one·2025

Same author

Insights from the eyes: a systematic review and meta-analysis of the intersection between eye-tracking and artificial intelligence in dementia.

Aging & mental health·2025

Search research articles

Related Experiment Video

Updated: May 7, 2026

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

A comprehensive comparison of different clustering methods for reliability analysis of microarray data.

Rahele Kafieh¹, Alireza Mehridehnavi

¹Department of Medical Physics and Engineering, Medical School, Medical Image and Signal Processing Research Center, Isfahan University of Medical Sciences, Isfahan, Iran.

Journal of Medical Signals and Sensors

|October 2, 2013

Summary

This summary is machine-generated.

This study evaluated competitive learning methods for microarray reliability analysis, finding the Rayleigh mixture model superior for clustering and reliability tasks. The Hopkins statistic was used to assess data

Keywords:

Clustering cluster validity microarrays reliability analysis

Related Experiment Videos

Last Updated: May 7, 2026

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

Area of Science:

Bioinformatics
Machine Learning
Statistical Analysis

Background:

Microarray data analysis requires robust reliability assessment.
Competitive learning algorithms offer potential for error minimization and entropy maximization.
Mixture decomposition schemes provide an alternative approach to function optimization.

Purpose of the Study:

To compare competitive learning methods (hard and soft) for microarray reliability analysis.
To investigate the efficacy of mixture decomposition schemes in this context.
To identify the most powerful algorithm based on defined criteria and similarity measures.

Main Methods:

Evaluation of hard and soft competitive learning algorithms with varying network dimensionality.
Application of mixture decomposition schemes, including the Rayleigh mixture model.
Utilizing numerical methods and matrix similarity measures for performance assessment.
Employing the Hopkins statistic to determine the intrinsic clusterability of datasets.

Main Results:

The Rayleigh mixture model demonstrated superior performance in reliability analysis compared to other evaluated methods.
The Hopkins statistic effectively indicated the dataset's suitability for clustering prior to analysis.
Competitive learning methods were assessed within a function optimization framework.

Conclusions:

The Rayleigh mixture model is a highly effective tool for microarray reliability analysis.
Assessing intrinsic data clusterability is crucial before applying clustering algorithms.
This study provides a framework for selecting optimal algorithms in bioinformatics data analysis.