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Rank-Similarity Measures for Comparing Gene Prioritizations: A Case Study in Autism.

Concettina Guerra1, Sarang Joshi1, Yinquan Lu1

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|October 26, 2020
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Summary

Comparing gene prioritization methods for disease association requires evaluating individual gene rankings. New similarity measures improve comparisons, effectively clustering brain regions by autism relevance.

Keywords:
autismcomparative analysisdisease–gene associationgene prioritization

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Gene prioritization methods are crucial for identifying disease-associated genes.
  • Existing comparison schemes for these methods may overlook individual gene ranking differences.
  • Network propagation, integer linear programming rank aggregation (RA), and statistical RA are common prioritization techniques.

Purpose of the Study:

  • To address the limitations of current gene prioritization method comparison schemes.
  • To introduce and evaluate novel similarity measures for gene rankings.
  • To improve the assessment of disease-gene association estimates.

Main Methods:

  • Comparison of network propagation, integer linear programming RA, and statistical RA methods.
  • Application of receiver operating curve, area under the curve, and median rank ratio metrics.
  • Integration of recently proposed similarity measures for gene rankings.

Main Results:

  • Standard comparison metrics may not fully capture performance differences between gene prioritization methods.
  • Novel similarity measures enhance the comparison of gene prioritization outcomes.
  • Effectively clustered brain regions based on their relevance to autism using the proposed measures.

Conclusions:

  • Similarity measures offer a more nuanced evaluation of gene prioritization methods.
  • Improved comparison schemes can better reveal biological insights, such as tissue-specific disease relevance.
  • This approach enhances the understanding of gene-disease associations for conditions like autism.