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Comparing Unlabeled Pedigree Graphs via Covering with Bipartite and Path.

Lamiaa A Amar1,2, Nahla A Belal3, Shaheera Rashwan4

  • 11 Informatic Research Institute , City SRTA, Alexandria, Egypt .

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology
|July 22, 2016
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Summary

Evaluating unlabeled subpedigrees is crucial for genetic testing. This study introduces algorithms for the Cover Unlabeled subPedigree with a Bipartite graph (CUPB) and Cover Unlabeled subPedigree with a Path (CUPP) problems to improve pedigree analysis.

Keywords:
bipartite graphisomorphismunlabeled pedigree

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Graph Theory

Background:

  • Family trees, or pedigrees, are vital for understanding genetic history and guiding diagnostic decisions.
  • Incomplete parent-child relationship data (2%-10%) can significantly alter pedigree graph structures.
  • Accurate pedigree evaluation is essential for reliable genetic risk assessment.

Purpose of the Study:

  • To address the challenge of unlabeled subpedigree isomorphism in large, generational family structures with external mating.
  • To develop efficient algorithms for specific subproblems within pedigree analysis.

Main Methods:

  • Focus on two restricted versions of the unlabeled subpedigree graph problem: CUPB and CUPP.
  • Application of fixed-parameter algorithms to solve these specific graph problems.
  • Analysis of isomorphism for large-scale, unlabeled subpedigrees.

Main Results:

  • Development of fixed-parameter algorithms for the CUPB and CUPP problems.
  • Provides a computational approach to handle missing data in pedigree analysis.
  • Enables more accurate comparison of large, complex family structures.

Conclusions:

  • The presented fixed-parameter algorithms offer efficient solutions for specific unlabeled subpedigree isomorphism problems.
  • These methods can enhance the accuracy and reliability of genetic risk assessments by improving pedigree analysis.
  • Addresses a critical need in bioinformatics for handling incomplete familial data in large datasets.