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Inference of transicriptional regulation network with gene expression data Kwon, Andrew Tae-Jun

Abstract

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-cyle related genes in yeast prepared by Spellman et al. Our algorithm, dubbed the event method, employs dynamic programming to determine the likelihood of a regulatory relationship between two genes based on their time-series expression data. We compare our algorithm 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 methods. We also compare the correlation and the event methods using synthetic data and find that typically, the event method obtains better results.

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