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Learning transcriptional networks from the integration of ChIP–chip and expression data in a non-parametric model

By Ahrim Youn, David J. Reiss and Werner Stuetzle

Abstract

Results: We have developed LeTICE (Learning Transcriptional networks from the Integration of ChIP–chip and Expression data), an algorithm for learning a transcriptional network from ChIP–chip and expression data. The network is specified by a binary matrix of transcription factor (TF)–gene interactions partitioning genes into modules and a background of genes that are not involved in the transcriptional regulation. We define a likelihood of a network, and then search for the network optimizing the likelihood

Topics: Original Papers
Publisher: Oxford University Press
OAI identifier: oai:pubmedcentral.nih.gov:2913654
Provided by: PubMed Central
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