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

Randomized Experiments01:13

Randomized Experiments

6.3K
The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
6.3K
Survival Tree01:19

Survival Tree

499
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
499
Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

4.0K
Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
4.0K
Genetic Screens02:46

Genetic Screens

4.6K
Genetic screens are tools used to identify genes and mutations responsible for phenotypes of interest. Genetic screens help identify individuals or a group of people at risk of developing  genetic diseases and help them with early intervention, targeted therapy, and reproductive options.
Forward genetic screens
Forward or “classical” genetic screens involve creating random mutations in an organism’s DNA using radiation, mutagens, or insertion of additional bases, which...
4.6K
Frequency-dependent Selection01:21

Frequency-dependent Selection

20.1K
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.
20.1K
Random and Systematic Errors01:20

Random and Systematic Errors

11.2K
Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...
11.2K

You might also read

Related Articles

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

Sort by
Same author

Spatio-temporal mechanisms of consolidation, recall and reconsolidation in reward-related memory trace.

Molecular psychiatry·2024
Same author

A tendency to worse course of multisystem inflammatory syndrome in children with obesity: MultiOrgan Inflammatory Syndromes COVID-19 related study.

Frontiers in endocrinology·2022
Same author

Use of broad-spectrum antibiotics in children diagnosed with multisystem inflammatory syndrome temporarily associated with SARS-CoV-2 infection in Poland: the MOIS-CoR study.

International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases·2022
Same author

Kendall transformation brings a robust categorical representation of ordinal data.

Scientific reports·2022
Same author

Distinct characteristics of multisystem inflammatory syndrome in children in Poland.

Scientific reports·2021
Same author

Prolonged Consumption of Sweetened Beverages Lastingly Deteriorates Cognitive Functions and Reward Processing in Mice.

Cerebral cortex (New York, N.Y. : 1991)·2021

Related Experiment Video

Updated: May 4, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.0K

Robustness of Random Forest-based gene selection methods.

Miron Bartosz Kursa1

  • 1Interdisciplinary Centre for Mathematical and Computational Modelling, University of Warsaw, Pawinskiego 5A, 02-106 Warsaw, Poland. M.Kursa@icm.edu.pl.

BMC Bioinformatics
|January 14, 2014
PubMed
Summary
This summary is machine-generated.

The Boruta algorithm offers the most stable gene selection for microarray data analysis, despite higher computational cost. This method is superior to others for identifying consistently important genes.

More Related Videos

A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research
09:35

A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research

Published on: August 16, 2017

16.6K
Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

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

Published on: March 1, 2024

1.6K

Related Experiment Videos

Last Updated: May 4, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.0K
A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research
09:35

A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research

Published on: August 16, 2017

16.6K
Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

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

Published on: March 1, 2024

1.6K

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Gene selection is crucial for understanding biological phenomena from microarray data.
  • Microarray data's inherent noise makes gene selection challenging.
  • Machine learning, particularly Random Forest, is often employed for gene selection.

Purpose of the Study:

  • To compare four state-of-the-art Random Forest-based feature selection methods for gene selection.
  • To evaluate the stability of gene selection, a critical but often overlooked aspect.
  • To assess the reliability of post-selection classifier error rate as a measure of gene selection quality.

Main Methods:

  • Comparison of four Random Forest-based feature selection algorithms.
  • Analysis focused on the stability and number of selected genes.
  • Evaluation of post-selection accuracy using Random Forest classifiers.

Main Results:

  • All methods showed equivalent post-selection accuracy.
  • Significant differences were observed in the number of selected genes and selection stability.
  • The Boruta algorithm identified the most potentially important genes.

Conclusions:

  • Post-selection classifier error rate can be a deceptive measure of gene selection quality.
  • The Boruta algorithm demonstrated superior performance in consistently selecting genes.
  • Boruta's computational intensity can be mitigated by using Random Ferns importance measures.