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Efficient sampling for Bayesian inference of conjunctive Bayesian networks
Author(s) -
Thomas Sakoparnig,
Niko Beerenwinkel
Publication year - 2012
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/bts433
Subject(s) - markov chain monte carlo , computer science , inference , bayesian probability , bayesian inference , bayes' theorem , graphical model , artificial intelligence , machine learning , computational biology , data mining , biology
Cancer development is driven by the accumulation of advantageous mutations and subsequent clonal expansion of cells harbouring these mutations, but the order in which mutations occur remains poorly understood. Advances in genome sequencing and the soon-arriving flood of cancer genome data produced by large cancer sequencing consortia hold the promise to elucidate cancer progression. However, new computational methods are needed to analyse these large datasets.

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