Some Relations Among Stochastic Finite State Networks Used in Automatic Speech Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Self-organizing maps
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Clustering Time-Series Gene Expression Data with Unequal Time Intervals
Transactions on Computational Systems Biology X
Microarray Time-Series Data Clustering via Multiple Alignment of Gene Expression Profiles
PRIB '09 Proceedings of the 4th IAPR International Conference on Pattern Recognition in Bioinformatics
Proceedings of the First ACM International Conference on Bioinformatics and Computational Biology
A new profile alignment method for clustering gene expression data
AI'06 Proceedings of the 19th international conference on Advances in Artificial Intelligence: Canadian Society for Computational Studies of Intelligence
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Microarrays allow monitoring of thousands of genes over time periods. Recently, gene clustering approaches specially adapted to deal with the time dependences of these data have been proposed. According to these methods, we investigate here how to use prior knowledge about the approximate profile of some classes to improve the classification result. We propose a Bayesian approach to this problem. A mixture model is used to describe and classify the data. The parameters of this model are constrained by a prior distribution defined with a new type of model that can express both our prior knowledge about the profile of classes of interest and the temporal nature of the data. Then, an EM algorithm estimates the parameters of the mixture model by maximizing its posterior probability. Supplementary Material: http://www.lirmm.fr/~brehelin/WABI05.pdf