Temporal Bayesian classifiers for modelling muscular dystrophy expression data

Issue title: Selected papers from IDA2005, Madrid, Spain

Article type: Research Article

Authors: Tucker, Allan^a | 't Hoen, Peter A.C.^b | Vinciotti, Veronica^a | Liu, Xiaohui^{a; c}

Affiliations: [a] School of Information Systems Computing and Mathematics, Brunel University, Uxbridge UB8 3PH, UK | [b] Center for Human and Clinical Genetics, Leiden University Medical Center, P.O. Box 9600, 2300 RC Leiden, The Netherlands | [c] Leiden Institute of Advanced Computer Science, Leiden University, P.O. Box 9512, 2300 RA Leiden, The Netherlands

Abstract: The 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.

DOI: 10.3233/IDA-2006-10504

Journal: Intelligent Data Analysis, vol. 10, no. 5, pp. 441-455, 2006