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Related Concept Videos

Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

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Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...
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Mass Spectrometry-Guided Genome Mining as a Tool to Uncover Novel Natural Products
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Unsupervised text mining for assessing and augmenting GWAS results.

Melissa Ailem1, François Role1, Mohamed Nadif1

  • 1LIPADE, Université Paris Descartes, Sorbonne Paris Cité, Paris F-75006, France.

Journal of Biomedical Informatics
|February 26, 2016
PubMed
Summary
This summary is machine-generated.

Text mining enhances genome-wide association studies (GWAS) by uncovering gene relationships for complex diseases. This approach identified significant asthma gene connections, aiding new candidate gene discovery.

Keywords:
AsthmaClusteringGWASUnsupervised text mining

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Area of Science:

  • Biomedical informatics
  • Genomics
  • Computational biology

Background:

  • Genome-wide association studies (GWAS) identify genes linked to multifactorial diseases but do not reveal relationships between them.
  • Text mining offers a method for analyzing and interpreting large-scale biomedical data, facilitating hypothesis confirmation.
  • Discovering functional relationships between genes is crucial for understanding complex diseases like asthma.

Purpose of the Study:

  • To augment GWAS results by leveraging unsupervised text mining techniques.
  • To characterize relationships between genes associated with asthma using text-based similarity and clustering.
  • To develop a generic framework for exploring gene-gene relationships in biomedical literature.

Main Methods:

  • Applied text mining, specifically cosine similarity and clustering, to gene vectors derived from literature.
  • Utilized candidate gene sets from GWAS and compared them against random gene sets.
  • Developed and applied a framework to analyze relationships among 10 asthma-associated genes.

Main Results:

  • Found significantly stronger similarities among the 10 asthma-associated genes than expected by chance (p<0.01).
  • Clustering analysis revealed potential functional relationships between these genes.
  • The text mining approach successfully generated hypotheses for new candidate asthma genes.

Conclusions:

  • Unsupervised text mining is effective in augmenting GWAS findings by identifying gene relationships.
  • The proposed framework can uncover potential functional connections and aid in discovering novel candidate genes.
  • This methodology provides a valuable tool for accelerating biomedical research and understanding complex diseases.