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Proceedings Paper

Statistical relationship discovery in SNP data using Bayesian networks
Author(s): Pawel Szlendak; Robert M. Nowak
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Paper Abstract

The aim of this article is to present an application of Bayesian networks for discovery of affinity relationships based on genetic data. The presented solution uses a search and score algorithm to discover the Bayesian network structure which best fits the data i.e. the alleles of single nucleotide polymorphisms detected by DNA microarrays. The algorithm perceives structure learning as a combinatorial optimization problem. It is a randomized local search algorithm, which uses a Bayesian-Dirichlet scoring function. The algorithm's testing procedure encompasses tests on synthetic data, generated from given Bayesian networks by a forward sampling procedure as well as tests on real-world genetic data. The comparison of Bayesian networks generated by the application and the genetic evidence data confirms the usability of the presented methods.

Paper Details

Date Published: 6 August 2009
PDF: 9 pages
Proc. SPIE 7502, Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2009, 75022J (6 August 2009); doi: 10.1117/12.837602
Show Author Affiliations
Pawel Szlendak, Warsaw Univ. of Technology (Poland)
Robert M. Nowak, Warsaw Univ. of Technology (Poland)


Published in SPIE Proceedings Vol. 7502:
Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2009
Ryszard S. Romaniuk; Krzysztof S. Kulpa, Editor(s)

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