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 Experiment Videos

Classification of arrayCGH data using fused SVM.

Franck Rapaport1, Emmanuel Barillot, Jean-Philippe Vert

  • 1Institut Curie, Centre de Recherche, INSERM, U900, Paris, F-75248 France. franck.rapaport@curie.fr

Bioinformatics (Oxford, England)
|July 1, 2008
PubMed
Summary
This summary is machine-generated.

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

Elevated levels of TNF and its targets characterize better-risk older acute myeloid leukemia patients.

Leukemia·2026
Same author

Mesenchymal to epithelial transition (MET) in cancer progression: insights from logical modeling.

Scientific reports·2026
Same author

The LMO2-LDB1-TAL1 complex regulates transcription networks in acute myeloid leukemia.

Blood neoplasia·2026
Same author

Multiomic integration reveals tumoral heterogeneity of lipid dependence within lethal group 3 medulloblastoma.

Cancer cell·2026
Same author

sCellST predicts single-cell gene expression from H& E images.

Nature communications·2026
Same author

Artificial miRNA-mediated reduction of SNCA for the treatment of α-synucleinopathies.

Molecular therapy : the journal of the American Society of Gene Therapy·2025
Same journal

3DICE: Interpretable 3D Cross-Modal Learning for Drug-Target Interaction Prediction and Large-Scale Drug Discovery.

Bioinformatics (Oxford, England)·2026
Same journal

KASSPer: Kinase Active Site Structure Prediction using Protein and Ligand Language Models and Its Application to Virtual Screening.

Bioinformatics (Oxford, England)·2026
Same journal

IDR searcher: a search engine solution for public image resources.

Bioinformatics (Oxford, England)·2026
Same journal

KCFtools: Rapid alignment-free method for introgression screening and GWAS using k-mer profiles.

Bioinformatics (Oxford, England)·2026
Same journal

Meta2DB: Curated shotgun metagenomic feature sets and metadata for health state prediction.

Bioinformatics (Oxford, England)·2026
Same journal

conMItion: an R package adjusting confounding factors for associations in multi-omics.

Bioinformatics (Oxford, England)·2026
See all related articles

This study introduces a novel support vector machine method for classifying array-based comparative genomic hybridization (arrayCGH) data. The approach enhances cancer diagnosis and prognosis by accurately identifying key genomic regions.

Area of Science:

  • Genomics
  • Bioinformatics
  • Cancer Research

Background:

  • Array-based comparative genomic hybridization (arrayCGH) is a key tool for identifying DNA copy number variations.
  • arrayCGH profiles are increasingly used as biomarkers for cancer diagnosis and prognosis.
  • Classical classification methods struggle with the unique correlational structure of arrayCGH data.

Purpose of the Study:

  • To develop an automated supervised classification method for arrayCGH data that accounts for genomic correlations.
  • To improve the accuracy and interpretability of arrayCGH data classification for cancer.
  • To identify biologically relevant genomic regions associated with cancer diagnosis and prognosis.

Main Methods:

  • A novel variant of the support vector machine (SVM) algorithm was developed.

Related Experiment Videos

  • The method incorporates biological specificities of DNA copy number variations as prior knowledge.
  • The approach results in a sparse linear classifier that selects a limited number of genomic regions.

    • Main Results:

    • The proposed method achieved more accurate predictions in classification tasks for bladder and uveal cancer.
    • The classifier identified known and novel regions of interest on the genome.
    • The resulting sparse linear classifier offers improved interpretability and identification of discriminative genomic regions.

    Conclusions:

    • The new SVM-based method effectively classifies arrayCGH data, improving accuracy and interpretability.
    • This approach enhances the utility of arrayCGH profiles for cancer diagnosis and prognosis.
    • The method facilitates the discovery of clinically relevant genomic regions.