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Related Experiment Video

Updated: May 26, 2026

In Vivo Modeling of the Morbid Human Genome using Danio rerio
12:31

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Published on: August 24, 2013

A vector space model approach to identify genetically related diseases.

Indra Neil Sarkar1

  • 1Center for Clinical and Translational Science, University of Vermont, Burlington, Vermont 05405, USA. neil.sarkar@uvm.edu

Journal of the American Medical Informatics Association : JAMIA
|January 10, 2012
PubMed
Summary

This study used vector space models to link gene and disease information from multiple databases, successfully identifying potential relationships for Alzheimer disease and Prader-Willi Syndrome.

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Area of Science:

  • Computational biology
  • Bioinformatics
  • Genetics

Background:

  • Complex polygenic diseases pose challenges due to intricate gene-disease relationships.
  • Genes can be causally linked to multiple diseases, complicating research.
  • Information retrieval techniques offer novel approaches to study these relationships.

Purpose of the Study:

  • To explore disease relationships using a vector space model (VSM) approach.
  • To adapt VSM for integrating gene-disease knowledge from diverse sources.
  • To identify potentially related diseases for Alzheimer disease and Prader-Willi Syndrome.

Main Methods:

  • Developed a VSM approach integrating data from Online Mendelian Inheritance in Man, GenBank, and Medline.
  • Applied the VSM to infer gene-disease knowledge.
  • Used the model to identify related diseases for Alzheimer disease and Prader-Willi Syndrome.

Main Results:

  • The VSM approach successfully identified plausible related diseases for both Alzheimer disease and Prader-Willi Syndrome.
  • These identified diseases warrant further investigation for potential links.
  • The relevance of predicted diseases was validated using supporting biomedical literature.

Conclusions:

  • Vector space modeling offers a viable method for uncovering potential relationships between complex diseases.
  • This approach facilitates the integration of gene-based findings across multiple complex diseases.
  • The study validates the utility of VSM in mining biomedical literature and genomic resources for disease correlations.