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

Mining OMIM for insight into complex diseases.

Michael N Cantor1, Yves A Lussier

  • 1Department of Biomedical Informatics, Columbia University, New York, NY 10032, USA.

Studies in Health Technology and Informatics
|September 14, 2004
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

Enhancing validation of case-control omics signatures through "minimalist" single-subject analysis (N-of-1 trials): proof of concept in sepsis.

Journal of the American Medical Informatics Association : JAMIA·2026
Same author

Transcriptomic evidence of remodeling in postpartum levator ani muscle associated with maternal age and recovery time after first vaginal delivery.

American journal of obstetrics and gynecology·2026
Same author

Paired-Sample and Pathway-Anchored MLOps Framework for Robust Transcriptomic Machine Learning in Small Cohorts: Model Classification Study.

JMIR bioinformatics and biotechnology·2025
Same author

Author Correction: Common and rare variant associations with clonal haematopoiesis phenotypes.

Nature·2025
Same author

NOTCH3 p.Arg1231Cys is markedly enriched in South Asians and associated with stroke.

Nature communications·2024
Same author

Genetic risk factors for COVID-19 and influenza are largely distinct.

Nature genetics·2024
Same journal

A GenAI Pipeline for Violinist Kinematic Data Management.

Studies in health technology and informatics·2026
Same journal

AMAL-For-Qatar: A Comprehensive AI Ecosystem for Fetal Ultrasound Analysis - Project Overview and Achievements.

Studies in health technology and informatics·2026
Same journal

Longitudinal Treatment-Aware Multimodal AI for Dermatology: A Scoping Review.

Studies in health technology and informatics·2026
Same journal

Predicting Postpartum Depression Using Imbalance-Aware Machine Learning.

Studies in health technology and informatics·2026
Same journal

Validation of Deep-Learning Models for Autosegmentation of Brain Metastases.

Studies in health technology and informatics·2026
Same journal

Delay-Dependent Gating in Modular RNNs.

Studies in health technology and informatics·2026
See all related articles

This study explores clustering clinical phenotypes to uncover underlying genetic causes for complex diseases. Findings suggest potential links between diabetes and progressive neurologic diseases and their genotypes.

Area of Science:

  • Genetics
  • Computational Biology
  • Clinical Research

Background:

  • Understanding genotype-phenotype correlations is crucial for genetic research, especially for complex diseases involving multiple genetic factors.
  • While gene expression microarrays link phenotypes to genotypes at the molecular level, clinical phenotype analysis remains less explored.
  • The Online Mendelian Inheritance in Man (OMIM) database offers extensive phenotypic and genetic data for such analyses.

Purpose of the Study:

  • To investigate if clustering clinical phenotypes can reveal insights into their underlying genetic architectures.
  • To evaluate a computational approach for analyzing complex disease phenotypes.
  • To identify potential genotype-phenotype relationships for diseases like diabetes and progressive neurologic disorders.

Main Methods:

Related Experiment Videos

  • Utilized the OMIM database as a comprehensive source of phenotypic and genetic information.
  • Applied self-organizing map (SOM) and hierarchical clustering analyses to diseases categorized by phenotype.
  • Developed pre-determined queries to analyze the clustering results for clinically significant findings.

Main Results:

  • Identified two key findings of potential clinical significance: one related to diabetes and another concerning progressive neurologic diseases.
  • Demonstrated that clustering clinical phenotypes can yield informative patterns relevant to genetic underpinnings.
  • The analysis highlighted specific disease groups where phenotype clustering may suggest shared genetic factors.

Conclusions:

  • The developed methods offer a formal approach to analyzing diverse clinical phenotypes for genotype elucidation.
  • Phenotype clustering analysis can guide future genetic research by pinpointing areas of potential interest.
  • This approach may accelerate the discovery of genetic causes for complex and multifactorial diseases.