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

Management of congenital duodenal obstruction in neonates: a 10-year retrospective cohort study from a National Children's Hospital.

Surgical endoscopy·2026
Same author

LncRNA MIR4435-2HG-mediated succinylation of USF1 promotes its protein stability and induces epithelial-mesenchymal transition in HNSCC.

Epigenetics·2026
Same author

Congenital H-type tracheoesophageal fistula: Experience from a large Chinese center.

Pediatric research·2026
Same author

Inflammation-guided timing of thoracoscopic repair for recurrent tracheoesophageal fistula: predictive value of preoperative chest CT and a transfer learning model.

BMC pediatrics·2026
Same author

Intraluminal bougienage-based elongation for native-preserving salvage in long-gap esophageal atresia.

Journal of pediatric surgery·2026
Same author

Diagnostic value of routine biochemical markers for biliary atresia in neonates with cholestasis: a retrospective study.

Translational pediatrics·2026
Same journal

circ2DGNN: circRNA-Disease Association Prediction via Transformer-Based Graph Neural Network.

IEEE/ACM transactions on computational biology and bioinformatics·2024
Same journal

Hierarchical Hypergraph Learning in Association- Weighted Heterogeneous Network for miRNA- Disease Association Identification.

IEEE/ACM transactions on computational biology and bioinformatics·2024
Same journal

Discriminative Domain Adaption Network for Simultaneously Removing Batch Effects and Annotating Cell Types in Single-Cell RNA-Seq.

IEEE/ACM transactions on computational biology and bioinformatics·2024
Same journal

MLW-BFECF: A Multi-Weighted Dynamic Cascade Forest Based on Bilinear Feature Extraction for Predicting the Stage of Kidney Renal Clear Cell Carcinoma on Multi-Modal Gene Data.

IEEE/ACM transactions on computational biology and bioinformatics·2024
Same journal

An End-to-End Knowledge Graph Fused Graph Neural Network for Accurate Protein-Protein Interactions Prediction.

IEEE/ACM transactions on computational biology and bioinformatics·2024
Same journal

Generative Biomedical Event Extraction With Constrained Decoding Strategy.

IEEE/ACM transactions on computational biology and bioinformatics·2024
See all related articles
  1. Home
  3. Feature Selection For Gene Expression Using Model-based Entropy.
  1. Home
  3. Feature Selection For Gene Expression Using Model-based Entropy.

Related Experiment Video

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets
03:37

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets

Published on: March 1, 2024

Feature selection for gene expression using model-based entropy.

Shenghuo Zhu1, Dingding Wang, Kai Yu

  • 1NEC Laboratories America, 10080 North Wolfe Road, Cupertino, CA 95014, USA. zsh@sv.nec-labs.com

IEEE/ACM Transactions on Computational Biology and Bioinformatics
|February 13, 2010

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces a novel model-based approach for gene expression feature selection, overcoming data sparseness issues. The method accurately identifies key genes for sample discrimination using multivariate Gaussian distributions.

More Related Videos

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

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2
10:10

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2

Published on: September 18, 2021

Related Experiment Videos

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets
03:37

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets

Published on: March 1, 2024

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

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2
10:10

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2

Published on: September 18, 2021

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Machine Learning

Background:

  • Gene expression datasets often feature numerous genes and few samples.
  • Traditional gene selection methods struggle with data sparseness, impacting accuracy.
  • Effective feature selection is crucial for discriminating biological sample types.

Purpose of the Study:

  • To develop a robust model-based approach for gene expression feature selection.
  • To address the data sparseness issue inherent in small sample-sized datasets.
  • To enhance the accuracy of identifying genes that best discriminate biological samples.

Main Methods:

  • Utilized multivariate normal distributions to model gene expression data.
  • Estimated entropy of class variables on the model rather than directly on sparse data.
  • Developed efficient algorithms to compute log-determinants for feature selection.
  • Applied a multivariate Gaussian generative model for feature selection.

    • Main Results:

    • The proposed model-based approach demonstrated high accuracy in feature selection across seven gene datasets.
    • Experimental comparisons showed superior performance compared to five other existing methods.
    • The developed algorithms significantly reduced computational costs for feature selection.

    Conclusions:

    • The multivariate Gaussian generative model offers an accurate and efficient solution for gene expression feature selection.
    • The proposed method effectively overcomes the limitations of traditional approaches, particularly data sparseness.
    • This approach provides a valuable tool for identifying biologically relevant genes from high-dimensional data.