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. © 2006-IOS Press and the authors.
Temporal Bayesian classifiers for modelling muscular dystrophy expression data / Tucker, A.; Hoen, P. A. C. '.; Vinciotti, V.; Liu, X.. - In: INTELLIGENT DATA ANALYSIS. - ISSN 1088-467X. - 10:5(2006), pp. 441-455. (Intervento presentato al convegno IDA2005 tenutosi a Madrid, Spain nel 8-10 September 2005) [10.3233/ida-2006-10504].
Temporal Bayesian classifiers for modelling muscular dystrophy expression data
Vinciotti V.;
2006-01-01
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. © 2006-IOS Press and the authors.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione
