Context-specific Bayesian clustering for gene expression data
RECOMB '01 Proceedings of the fifth annual international conference on Computational biology
Cellular computation and communications using engineered genetic regulatory networks
Cellular computation and communications using engineered genetic regulatory networks
Inference of transcriptional regulation relationships from gene expression data
Proceedings of the 2003 ACM symposium on Applied computing
Inference of transcriptional regulation relationships from gene expression data
Proceedings of the 2003 ACM symposium on Applied computing
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We propose a new method for finding potential regulatory relationships between pairs of genes from microarray time series data and apply it to expression data for cell-cycle related genes in yeast. We compare our algorithm, dubbed the event method, with the earlier correlation method and the edge detection method by Filkov et al. When tested on known transcriptional regulation genes, all three methods are able to find similar numbers of true positives. The results indicate that our algorithm is able to identify true positive pairs that are different from those found by the two other methods. We also compare the correlation and the event methods using synthetic data and find that typically, the event method obtains better results.