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An information theoretic approach to pedigree reconstruction

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  • Almudevar, Anthony

Abstract

Network structure is a dominant feature of many biological systems, both at the cellular level and within natural populations. Advances in genotype and gene expression screening made over the last few decades have permitted the reconstruction of these networks. However, resolution to a single model estimate will generally not be possible, leaving open the question of the appropriate method of formal statistical inference. The nonstandard structure of the problem precludes most traditional statistical methodologies. Alternatively, a Bayesian approach provides a natural methodology for formal inference. Construction of a posterior density on the space of network structures allows formal inference regarding features of network structure using specific marginal posterior distributions.

Suggested Citation

  • Almudevar, Anthony, 2016. "An information theoretic approach to pedigree reconstruction," Theoretical Population Biology, Elsevier, vol. 107(C), pages 52-64.
    • Handle: RePEc:eee:thpobi:v:107:y:2016:i:c:p:52-64
    DOI: 10.1016/j.tpb.2015.09.006
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    References listed on IDEAS

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    1. Matthieu Vignes & Jimmy Vandel & David Allouche & Nidal Ramadan-Alban & Christine Cierco-Ayrolles & Thomas Schiex & Brigitte Mangin & Simon de Givry, 2011. "Gene Regulatory Network Reconstruction Using Bayesian Networks, the Dantzig Selector, the Lasso and Their Meta-Analysis," PLOS ONE, Public Library of Science, vol. 6(12), pages 1-15, December.
    2. Cowell, Robert G., 2009. "Efficient maximum likelihood pedigree reconstruction," Theoretical Population Biology, Elsevier, vol. 76(4), pages 285-291.
    3. Ellis, Byron & Wong, Wing Hung, 2008. "Learning Causal Bayesian Network Structures From Experimental Data," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 778-789, June.
    4. Scutari, Marco, 2010. "Learning Bayesian Networks with the bnlearn R Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 35(i03).
    5. Almudevar, Anthony & LaCombe, Jason, 2012. "On the choice of prior density for the Bayesian analysis of pedigree structure," Theoretical Population Biology, Elsevier, vol. 81(2), pages 131-143.
    6. Sheehan, Nuala A. & Bartlett, Mark & Cussens, James, 2014. "Improved maximum likelihood reconstruction of complex multi-generational pedigrees," Theoretical Population Biology, Elsevier, vol. 97(C), pages 11-19.
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