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

Cluster Sampling Method01:20

Cluster Sampling Method

13.7K
Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
13.7K
Ranks01:02

Ranks

337
Unlike parametric methods, nonparametric statistics are ideal for nominal and ordinal data, requiring fewer assumptions about the population's nature or distribution. This makes nonparametric methods easier to apply and interpret, as they do not depend on parameters like mean or standard deviation. One common approach in nonparametric analysis is to sort data according to a specific criterion. For instance, we might arrange weather data from hottest to coldest days in a month or rank cities...
337
Aggregates Classification01:29

Aggregates Classification

558
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
558
Spearman's Rank Correlation Test01:20

Spearman's Rank Correlation Test

1.2K
Spearman's rank correlation test, also known as Spearman's rho, is a nonparametric method for assessing the strength and direction of association between two variables. This test is particularly valuable when the data distribution is unknown or when the assumption of normality does not hold. Named after the English psychologist and statistician Dr. Charles Edward Spearman, it serves as the nonparametric counterpart to Pearson's correlation coefficient.
Spearman's test calculates correlation by...
1.2K
Classification of Systems-II01:31

Classification of Systems-II

371
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
371
Sieve Analysis and Grading Curves01:19

Sieve Analysis and Grading Curves

727
Sieve analysis is a method used to determine the particle size distribution of aggregate materials. This process involves the following steps:
727

You might also read

Related Articles

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

Sort by
Same author

Network anomaly detection using Deep Autoencoder and parallel Artificial Bee Colony algorithm-trained neural network.

PeerJ. Computer science·2026
Same author

In Vivo CAR-T Therapies-A New Era of Programmable Immunity.

International journal of molecular sciences·2026
Same author

Correction: Engineering novel features for diabetes complication prediction using synthetic electronic health records.

Frontiers in genetics·2025
Same author

Proinflammatory Effect of Mesenchymal Stem Cells From Patients With Multiple Sclerosis: Potential Modulation Targets.

Neurology(R) neuroimmunology & neuroinflammation·2025
Same author

Cutaneous T-Cell Lymphoma: Yin-Yang Effects of Transcription Factors HLF and NFIL3 in Regulation of Malignant T-Cell Markers in the Context of HDAC Inhibitor Romidepsin Treatment.

Cancers·2025
Same author

3Mont: A multi-omics integrative tool for breast cancer subtype stratification.

PloS one·2025

Related Experiment Video

Updated: Nov 19, 2025

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

7.2K

Recursive Cluster Elimination based Rank Function (SVM-RCE-R) implemented in KNIME.

Malik Yousef1, Burcu Bakir-Gungor2, Amhar Jabeer2

  • 1Zefat Academic College, Zefat, Israel.

F1000Research
|January 28, 2021
PubMed
Summary

We developed SVM-RCE-R, an enhanced feature selection method for biological datasets. It allows customizable gene weighting for improved accuracy and specificity in analyses.

Keywords:
KNIMEclusteringgene expressiongroupingmachine learningrankingrecursive

More Related Videos

Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks
09:49

Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks

Published on: September 25, 2021

4.6K
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

7.8K

Related Experiment Videos

Last Updated: Nov 19, 2025

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

7.2K
Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks
09:49

Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks

Published on: September 25, 2021

4.6K
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

7.8K

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Machine Learning in Genomics

Background:

  • Recursive Cluster Elimination with Support Vector Machines (SVM-RCE) is a previously developed feature selection approach.
  • Growing interest and publications highlight the utility of SVM-RCE in various studies.
  • Previous methods did not fully leverage gene relationships in biological datasets during feature selection.

Purpose of the Study:

  • To enhance SVM-RCE by incorporating a user-defined ranking function for flexible feature selection.
  • To improve the selection of relevant genes in biological datasets by considering their relationships.
  • To develop a user-friendly implementation of the enhanced algorithm.

Main Methods:

  • Development of SVM-RCE-R, an extension of SVM-RCE, featuring a novel user-specified ranking function.
  • The ranking function allows users to assign weights to metrics like accuracy, sensitivity, specificity, f-measure, AUC, and precision.
  • Implementation of SVM-RCE-R in the KNIME platform for accessible application, accepting common file formats (Excel, CSV, text).

Main Results:

  • The inclusion of the customizable ranking function significantly impacts the performance of SVM-RCE-R.
  • SVM-RCE-R demonstrates flexibility, enabling users to prioritize sensitivity or specificity as project needs dictate.
  • The maTE tool, utilizing a similar approach, has been developed for identifying microRNA targets, showcasing broader applicability.

Conclusions:

  • SVM-RCE-R offers a significant advancement in feature selection for biological data by allowing tailored analysis based on user-defined priorities.
  • The KNIME implementation makes advanced feature selection accessible to researchers without specialized IT skills.
  • The customizable weighting system provides a powerful tool for optimizing gene selection based on specific research objectives.