Discovery of Regulatory Connections in Microarray Data

Abstract. In this paper, we introduce a new approach for mining regulatory interactions between genes in microarray time series studies. A number of preprocessing steps transform the original continuous measurements into a discrete representation that captures salient regulatory events in the time series. The discrete representation is used to discover interactions between the genes. In particular, we introduce a new across-model sampling scheme for performing Markov Chain Monte Carlo sampling of probabilistic network classifiers. The results obtained from the microarray data are promising. Our approach can detect interactions caused both by co-regulation and by control-regulation.

• Type: Conference
• Year: 2004
• Where: PKDD
• Authors: Michael Egmont-Petersen, Wim de Jonge, Arno Siebes
• Keywords: Discrete Representation, Mining Regulatory Interactions, PKDD 2004, Time Series