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Distributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing, and Ambient Assisted Living

Volume 5518 of the series Lecture Notes in Computer Science pp 1109-1117

Bayesian Joint Estimation of CN and LOH Aberrations

  • Paola M. V. RancoitaAffiliated withIstituto Dalle Molle di Studi sull’Intelligenza ArtificialeLaboratory of Experimental Oncology, Oncology Institute of Southern SwitzerlandDipartimento di Matematica, Università degli Studi di Milano
  • , Marcus HutterAffiliated withRSISE, ANU and SML, NICTA
  • , Francesco BertoniAffiliated withLaboratory of Experimental Oncology, Oncology Institute of Southern Switzerland
  • , Ivo KweeAffiliated withIstituto Dalle Molle di Studi sull’Intelligenza ArtificialeLaboratory of Experimental Oncology, Oncology Institute of Southern Switzerland

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Abstract

SNP-microarrays are able to measure simultaneously both copy number and genotype at several single nucleotide polymorphism positions. Combining the two data, it is possible to better identify genomic aberrations. For this purpose, we propose a Bayesian piecewise constant regression which infers the type of aberration occurred, taking into account all the possible influence in the microarray detection of the genotype, resulting from an altered copy number level. Namely, we model the distributions of the detected genotype given a specific genomic alteration and we estimate the hyper-parameters used on public reference datasets.

Keywords

Bayesian regression piecewise constant function change point problem DNA copy number estimation LOH estimation