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The Network of Genetic Admixture in Humans

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Complex Networks VII

Part of the book series: Studies in Computational Intelligence ((SCI,volume 644))

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Abstract

Recent advances in the field of genetic data analysis reveal promising findings in the field of human history; especially when combined with proper data analysis tools. Within the field of modern genetics, there is evidence that the human populations have genetically interacted as a result of several events. The genetic admixture contains multiple pieces of DNA that have been passed down subsequently through generations making it combine DNA from different source groups. In this paper, we construct and analyze the network of human genetic admixture. We study the topology of this network, we investigate its \(\delta \)-hyperbolicity (negative curvature), and, using it, identify the core vertices by proposing the \(\delta \)-hyperbolicity-neighborhood measure that we assign to each vertex.

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Notes

  1. 1.

    Note that the value of r could be higher but never exceeds \(6\delta (G)+2\) [2]. However, for real-networks it was observed in [2] that \(r \approx \delta (G)\).

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Authors and Affiliations

  1. Department of Computer Science, Kent State University, Kent, OH, 44242, USA

    Hend Alrasheed & Feodor F. Dragan

Authors
  1. Hend Alrasheed
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  2. Feodor F. Dragan
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Corresponding author

Correspondence to Hend Alrasheed .

Editor information

Editors and Affiliations

  1. LE2I , UFR Sciences et Techniques, Université de Bourgogne, Dijon, France

    Hocine Cherifi

  2. New York University, Center for Data Science, New York, USA

    Bruno Gonçalves

  3. BioComplex Laboratory, Computer Sciences, Florida Institute of Technology, Melbourne, FL, Florida, USA

    Ronaldo Menezes

  4. 111 Dana, Physics Department, Northeastern University, Boston, Massachusetts, USA

    Roberta Sinatra

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Alrasheed, H., Dragan, F.F. (2016). The Network of Genetic Admixture in Humans. In: Cherifi, H., Gonçalves, B., Menezes, R., Sinatra, R. (eds) Complex Networks VII. Studies in Computational Intelligence, vol 644. Springer, Cham. https://doi.org/10.1007/978-3-319-30569-1_18

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  • DOI: https://doi.org/10.1007/978-3-319-30569-1_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-30568-4

  • Online ISBN: 978-3-319-30569-1

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