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

A segmentation/clustering model for the analysis of array CGH data.

F Picard1, S Robin, E Lebarbier

  • 1UMR INA P-G/ENGREF/INRA MIA 518, Paris, France. picard@inapg.fr

Biometrics
|September 11, 2007
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

Fetal alcohol spectrum disorder and health professionals' awareness of the syndrome: A comparison of practitioners' knowledge in two french regions.

Archives de pediatrie : organe officiel de la Societe francaise de pediatrie·2025
Same author

Kernel-based testing for single-cell differential analysis.

Genome biology·2024
Same author

Transcriptomic basis of sex loss in the pea aphid.

BMC genomics·2024
Same author

[Digital transformation of perioperative nurse-coordinated protocols in renal surgery for enhanced recovery and outpatient surgery using UroConnect® application].

Progres en urologie : journal de l'Association francaise d'urologie et de la Societe francaise d'urologie·2022
Same author

Remote patient monitoring for chronic heart failure in France: When an innovative funding program (ETAPES) meets an innovative solution (Satelia® Cardio).

Digital health·2022
Same author

[Describing and disseminating organizational innovation in public health DINOSP ; guide (Description of Organizational Innovations in Public Health)].

Revue d'epidemiologie et de sante publique·2022
Same journal

Fast penalized generalized estimating equations for large longitudinal functional datasets.

Biometrics·2026
Same journal

Causally-interpretable random-effects meta-analysis.

Biometrics·2026
Same journal

Statistical inference for mean function of partially observed functional time series.

Biometrics·2026
Same journal

Subgroup identification via Interaction Tree and Mixed Model for Repeated Measures with application to Alzheimer's disease.

Biometrics·2026
Same journal

Finite mixtures of linear quantile regressions with concomitant variables: a solution to endogeneity in longitudinal data modeling.

Biometrics·2026
Same journal

Discussion on "INTACT: a method for integration of longitudinal physical activity data from multiple sources" by Jingru Zhang, Erjia Cui, Hongzhe Li, and Haochang Shou.

Biometrics·2026
See all related articles

This study introduces a novel segmentation/clustering model for analyzing chromosomal imbalances detected by microarray-comparative genomic hybridization (CGH). The new dynamic programming-expectation maximization (DP-EM) algorithm effectively segments genomic data and assigns biological status.

Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Microarray-based comparative genomic hybridization (CGH) is crucial for detecting chromosomal imbalances.
  • Existing segmentation methods for CGH data lack biological interpretation of detected segments.
  • Genomic profiles are often represented as segments of homogeneous regions with similar copy numbers.

Purpose of the Study:

  • To develop a novel model integrating segmentation and mixture models for CGH data analysis.
  • To introduce a hybrid dynamic programming-expectation maximization (DP-EM) algorithm for parameter estimation.
  • To propose a model selection heuristic for determining the optimal number of segments and clusters.

Main Methods:

  • A hybrid DP-EM algorithm combining dynamic programming and expectation-maximization was developed.

Related Experiment Videos

  • The model integrates segmentation with mixture modeling to provide biological status to genomic segments.
  • A heuristic approach was used for model selection, including the number of segments and clusters.

    • Main Results:

    • The proposed DP-EM algorithm effectively estimates model parameters via maximum likelihood.
    • The new segmentation/clustering model demonstrates a promising alternative to existing methods.
    • Comparative analysis showed superior performance against traditional segmentation methods and hidden Markov models.

    Conclusions:

    • The developed segmentation/clustering model offers enhanced biological interpretation for CGH data.
    • The DP-EM algorithm provides an efficient method for analyzing chromosomal imbalances.
    • This approach has broader applicability in signal processing beyond genomic analysis.