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Liverani, S; Anderson, PE; Edwards, KD; Millar, AJ; Smith, JQ (2009)
Publisher: International Society for Bayesian Analysis (ISBA)
Languages: English
Types: Article
Subjects: QA, Circardian Expression Profiles, Bayesian, Genetics, Posterior Probability Distribution

Classified by OpenAIRE into

ACM Ref: ComputingMethodologies_PATTERNRECOGNITION
Because of the huge number of partitions of even a moderately sized dataset, even when Bayes factors have a closed form, in model-based clustering a comprehensive search for the highest scoring (MAP) partition is usually impossible. However, when each cluster in a partition has a signature and it is known that some signatures are of scientific interest whilst others are not, it is possible, within a Bayesian framework, to develop search algorithms which are guided by these cluster signatures. Such algorithms can be expected to find better partitions more quickly. In this paper we develop a framework within which these ideas can be formalized. We then briefly illustrate the efficacy of the proposed guided search on a microarray time coursed at a set where the clustering objective is to identify clusters of genes with different types of circadian expression profiles.
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