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

Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

1.5K
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...
1.5K
Weighted Mean00:57

Weighted Mean

4.9K
While taking the arithmetic, geometric, or harmonic mean of a sample data set, equal importance is assigned to all the data points. However, all the values may not always be equally important in some data sets. An intrinsic bias might make it more important to give more weightage to specific values over others.
For example, consider the number of goals scored in the matches of a tournament. While computing the average number of goals scored in the tournament, it may be more important to...
4.9K
Expected Frequencies in Goodness-of-Fit Tests01:19

Expected Frequencies in Goodness-of-Fit Tests

2.5K
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).
2.5K
Regression Toward the Mean01:52

Regression Toward the Mean

6.3K
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
6.3K
Wilcoxon Signed-Ranks Test for Median of Single Population01:14

Wilcoxon Signed-Ranks Test for Median of Single Population

104
The Wilcoxon signed-rank test for the median of a single population is a nonparametric test used to evaluate whether the median of a population differs from a specified value. Unlike parametric tests, it does not require data to follow a normal distribution, making it suitable for non-normal or small samples. The test begins by calculating the difference (d) between each observation and the hypothesized median. The absolute values of these differences are ranked in ascending order, with ties...
104
Sensitivity, Specificity, and Predicted Value01:13

Sensitivity, Specificity, and Predicted Value

213
In healthcare diagnostics, laboratory tests play a crucial role in identifying and diagnosing a wide range of medical conditions. However, interpreting test results is not always straightforward. An abnormal test result does not always confirm the presence of a disease, just as a normal result does not guarantee its absence. To assess the reliability of these diagnostic tools, healthcare practitioners rely on two key statistical indicators: sensitivity and specificity.
Sensitivity is the...
213

You might also read

Related Articles

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

Sort by
Same author

Double weighted k nearest neighbours for binary classification of high dimensional genomic data.

Scientific reports·2025
Same author

Feature selection via robust weighted score for high dimensional binary class-imbalanced gene expression data.

Heliyon·2024
Same author

Regularized ensemble learning for prediction and risk factors assessment of students at risk in the post-COVID era.

Scientific reports·2024
Same author

Feature selection for high dimensional microarray gene expression data via weighted signal to noise ratio.

PloS one·2023
Same author

Prediction of Multidrug-Resistant Tuberculosis Using Machine Learning Algorithms in SWAT, Pakistan.

Journal of healthcare engineering·2021
Same author

Forecasting COVID-19 in Pakistan.

PloS one·2020

Related Experiment Video

Updated: Jun 13, 2025

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

7.4K

Margin weighted robust discriminant score for feature selection in imbalanced gene expression classification.

Sheema Gul1, Dost Muhammad Khan1, Saeed Aldahmani2

  • 1Department of Statistics, Abdul Wali Khan University, Mardan, Pakistan.

Plos One
|June 10, 2025
PubMed
Summary
This summary is machine-generated.

A new feature selection method, Margin Weighted Robust Discriminant Score (MW-RDS), effectively handles high-dimensional imbalanced gene expression data. MW-RDS improves classification accuracy by amplifying minority class influence and reducing redundancy.

More Related Videos

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

655
Assisted Selection of Biomarkers by Linear Discriminant Analysis Effect Size LEfSe in Microbiome Data
04:57

Assisted Selection of Biomarkers by Linear Discriminant Analysis Effect Size LEfSe in Microbiome Data

Published on: May 16, 2022

15.6K

Related Experiment Videos

Last Updated: Jun 13, 2025

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

7.4K
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

655
Assisted Selection of Biomarkers by Linear Discriminant Analysis Effect Size LEfSe in Microbiome Data
04:57

Assisted Selection of Biomarkers by Linear Discriminant Analysis Effect Size LEfSe in Microbiome Data

Published on: May 16, 2022

15.6K

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Machine Learning

Background:

  • High-dimensional gene expression data presents challenges for binary classification.
  • Existing feature selection methods struggle with class imbalance and feature redundancy.

Purpose of the Study:

  • To propose a robust feature selection method for high-dimensional imbalanced data.
  • To enhance gene/feature discriminative power and class separation.

Main Methods:

  • Introduced Margin Weighted Robust Discriminant Score (MW-RDS).
  • Integrated a minority amplification factor and class-specific stability weights.
  • Utilized margin weights from support vectors and L1-regularization.

Main Results:

  • MW-RDS demonstrated superior performance over existing methods on 9 gene expression datasets.
  • Evaluated using Random Forest, SVM, and Weighted kNN classifiers.
  • Achieved improved accuracy, sensitivity, specificity, F1-score, and precision.

Conclusions:

  • MW-RDS is a robust and effective feature selection method for high-dimensional imbalanced problems.
  • The method successfully addresses class imbalance and redundancy issues.
  • Outperforms conventional approaches in gene expression data classification.