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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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Published on: October 11, 2018

A Classifier-based approach to identify genetic similarities between diseases.

Marc A Schaub1, Irene M Kaplow, Marina Sirota

  • 1Department of Computer Science, Stanford University, Stanford, CA 94305, USA.

Bioinformatics (Oxford, England)
|May 30, 2009
PubMed
Summary

This study introduces a new method to find disease similarities using genetic data from many single nucleotide polymorphisms (SNPs). The approach successfully identified known links and a new potential connection between bipolar disease and hypertension.

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Area of Science:

  • Genetics
  • Computational Biology
  • Medical Informatics

Background:

  • Genome-wide association studies (GWAS) typically identify individual genetic variations (SNPs) linked to specific diseases.
  • Existing methods focus on single disease-SNP associations, limiting the discovery of complex disease relationships.
  • A novel approach is needed to leverage large SNP datasets for understanding inter-disease genetic similarities.

Purpose of the Study:

  • To develop and validate a novel computational methodology for identifying similarities between different diseases using genome-wide SNP data.
  • To establish a framework for ranking disease similarities based on classifier performance across multiple diseases.
  • To uncover previously unknown genetic links between common diseases.

Main Methods:

  • A novel classification-based approach was developed to compare diseases using genotype data.
  • Diseases were categorized as reference or query, with a classifier trained to distinguish cases from controls for the reference disease.
  • The trained classifier was applied to query diseases to assess their similarity to the reference disease, with each disease serving as a reference iteratively.
  • A decision tree classifier was employed using genotype data from seven common diseases.

Main Results:

  • The methodology successfully identified the established genetic similarity between type 1 diabetes and rheumatoid arthritis.
  • A novel putative genetic similarity was discovered between bipolar disease and hypertension.
  • The approach demonstrated the utility of using machine learning classifiers on large SNP datasets to infer disease relationships.

Conclusions:

  • The developed methodology provides a powerful new tool for uncovering genetic similarities between diseases.
  • This approach can enhance our understanding of disease etiology and potentially inform diagnostic or therapeutic strategies.
  • The findings highlight the potential for computational methods to reveal complex patterns in genetic association data across multiple conditions.