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

You might also read

Related Articles

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

Sort by
Same author

Protecting Vaccine Safety: An Improved, Blockchain-Based, Storage-Efficient Scheme.

IEEE transactions on cybernetics·2022
Same author

A consensus multi-view multi-objective gene selection approach for improved sample classification.

BMC bioinformatics·2020
Same author

A Refined 3-in-1 Fused Protein Similarity Measure: Application in Threshold-Free Hub Detection.

IEEE/ACM transactions on computational biology and bioinformatics·2020
Same author

Novel symmetry-based gene-gene dissimilarity measures utilizing Gene Ontology: Application in gene clustering.

Gene·2018
Same author

Multi-Factored Gene-Gene Proximity Measures Exploiting Biological Knowledge Extracted from Gene Ontology: Application in Gene Clustering.

IEEE/ACM transactions on computational biology and bioinformatics·2018
Same author

Simultaneous Clustering and Feature Weighting Using Multiobjective Optimization for Identifying Functionally Similar miRNAs.

IEEE journal of biomedical and health informatics·2018

Related Experiment Video

Updated: Nov 23, 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

7.8K

Multi-view feature selection for identifying gene markers: a diversified biological data driven approach.

Sudipta Acharya1, Laizhong Cui2, Yi Pan3

  • 1College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, People's Republic of China.

BMC Bioinformatics
|December 30, 2020
PubMed
Summary

This study introduces UMVMO-select, a novel unsupervised multi-view multi-objective clustering approach for efficient gene selection. It identifies crucial non-redundant marker genes from high-dimensional cancer data, improving disease understanding.

Keywords:
Gene ontology (GO)Gene selectionGene similarity measuresMulti-objective clusteringMulti-view learningProtein–protein interaction network (PPIN)Sample classification

More Related Videos

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts
08:51

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Published on: September 20, 2024

1.8K
Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
07:41

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

Published on: May 17, 2019

9.3K

Related Experiment Videos

Last Updated: Nov 23, 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

7.8K
Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts
08:51

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Published on: September 20, 2024

1.8K
Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
07:41

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

Published on: May 17, 2019

9.3K

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • High-dimensional gene expression data presents challenges for feature selection.
  • Integrating multiple genomic and proteomic data sources is crucial for understanding complex diseases like cancer.

Purpose of the Study:

  • To develop an efficient feature selection algorithm for identifying relevant and non-redundant marker genes.
  • To leverage multi-view biological data for improved gene selection in cancer research.

Main Methods:

  • Feature selection framed as a multi-view multi-objective clustering problem.
  • Proposed Unsupervised Multi-View Multi-Objective clustering-based gene selection (UMVMO-select) approach.
  • Utilized gene ontology, protein interaction, and protein sequence data alongside gene expression values to create two distinct views.

Main Results:

  • UMVMO-select effectively reduces gene space while maintaining sample classification efficiency.
  • Identified relevant and non-redundant gene markers from three cancer gene expression benchmark datasets.
  • The proposed method demonstrated superior performance compared to existing clustering and feature selection techniques.

Conclusions:

  • UMVMO-select outperforms existing methods in gene selection and marker detection.
  • Results were validated through biological significance testing and heatmap analysis.
  • The approach offers a robust method for analyzing complex biological data.