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

Polygenic Traits01:18

Polygenic Traits

71.1K
When more than one gene is responsible for a given phenotype, the trait is considered polygenic. Human height is a polygenic trait. Studies have uncovered hundreds of loci that influence height, and there are believed to be many more. Due to the high number of genes involved, as well as environmental and nutritional factors, height varies significantly within a given population. The distribution of height forms a bell-shaped curve, with relatively few individuals in the population at the...
71.1K

You might also read

Related Articles

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

Sort by
Same author

Equivariant Filters for Efficient Tracking in 3D Imaging.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention·2026
Same author

Evaluating reliability of automated quantitative brain morphometry from fetal T2-weighted MRI.

Frontiers in neuroscience·2026
Same author

Toward real-time alignment of 3D CT and 2D X-ray with multi-stage CNNs.

Computer assisted surgery (Abingdon, England)·2026
Same author

Respiratory Oscillometry in COPD Patients with Airway Mucus Plugs: Insights from the ECLIPSE Study.

American journal of respiratory and critical care medicine·2026
Same author

Rare coding variant architecture and gene discovery from 130,000 sequenced cases of atrial fibrillation.

Research square·2026
Same author

Recent innovations in placental MRI: Integrating visualization and functional imaging.

Placenta·2026

Related Experiment Video

Updated: Apr 16, 2026

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

16.5K

Spherical Topic Models for Imaging Phenotype Discovery in Genetic Studies.

Kayhan N Batmanghelich1, Michael Cho2, Raul San Jose

  • 1Computer Science and Artificial Intelligence Lab., MIT.

Bayesian and Graphical Models for Biomedical Imaging : First International Workshop, BAMBI 2014, Cambridge, MA, USA, September 18, 2014 ; Revised Selected Papers. BAMBI (Workshop) (1St : 2014 : Cambridge, Mass.)
|February 27, 2015
PubMed
Summary

Spherical Topic Models reveal lung disease structure using normalized histograms. This approach improves genetic marker discovery for Chronic Obstructive Pulmonary Disease (COPD) in Genome Wide Association Studies (GWAS).

More Related Videos

A 3D Spheroid Model for Glioblastoma
07:40

A 3D Spheroid Model for Glioblastoma

Published on: April 9, 2020

16.6K
Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps
08:59

Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps

Published on: October 28, 2018

7.7K

Related Experiment Videos

Last Updated: Apr 16, 2026

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

16.5K
A 3D Spheroid Model for Glioblastoma
07:40

A 3D Spheroid Model for Glioblastoma

Published on: April 9, 2020

16.6K
Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps
08:59

Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps

Published on: October 28, 2018

7.7K

Area of Science:

  • Computational biology
  • Medical imaging analysis
  • Genetics

Background:

  • Chronic Obstructive Pulmonary Disease (COPD) exhibits complex heterogeneity.
  • Existing methods for genetic marker discovery in COPD may not fully capture disease variability.
  • Image-derived features offer potential to represent disease heterogeneity.

Purpose of the Study:

  • To apply Spherical Topic Models for discovering the latent structure of lung disease.
  • To utilize image-derived normalized histograms as phenotypes for genetic association analysis.
  • To enhance the identification of genetic variants linked to COPD.

Main Methods:

  • Implementation of Spherical Topic Models using normalized histograms of image intensity.
  • Generation of descriptive features representing disease heterogeneity.
  • Application of these features as phenotypes in Genome Wide Association Studies (GWAS).

Main Results:

  • The developed generative model successfully represents intensity distributions as latent factors and mixing coefficients.
  • The new features enhanced a previously detected genetic signal on chromosome 15 for COPD.
  • The approach demonstrated improved detection compared to standard respiratory and imaging measurements.

Conclusions:

  • Spherical Topic Models provide a robust method for analyzing histogram-based data in biomedical research.
  • The use of image-derived features as phenotypes can improve the power of GWAS for complex diseases like COPD.
  • This methodology offers a promising avenue for dissecting the genetic architecture of multifactorial lung diseases.