Please use this identifier to cite or link to this item: http://buratest.brunel.ac.uk/handle/2438/3227
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dc.contributor.authorTucker, A-
dc.contributor.authorHoen, PAC't-
dc.contributor.authorVinciotti, V-
dc.contributor.authorLiu, X-
dc.coverage.spatial15en
dc.date.accessioned2009-04-24T14:28:21Z-
dc.date.available2009-04-24T14:28:21Z-
dc.date.issued2006-
dc.identifier.citationIntelligent Data Analysis. 10(5): 441-455en
dc.identifier.issn1088-467X-
dc.identifier.urihttp://bura.brunel.ac.uk/handle/2438/3227-
dc.description.abstractThe analysis of microarray data from time-series experiments requires specialised algorithms, which take the temporal ordering of the data into account. In this paper we explore a new architecture of Bayesian classifier that can be used to understand how biological mechanisms differ with respect to time. We show that this classifier improves the classification of microarray data and at the same time ensures that the models can easily be analysed by biologists by incorporating time transparently. In this paper we focus on data that has been generated to explore different types of muscular dystrophy.en
dc.format.extent307345 bytes-
dc.format.mimetypeapplication/pdf-
dc.language.isoen-
dc.publisherIOS Press-
dc.titleTemporal Bayesian classifiers for modelling muscular dystrophy expression dataen
dc.typeResearch Paperen
Appears in Collections:Computer Science
Dept of Computer Science Research Papers

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