Published 2005 in Journal of Computational Biology 12(2), pp. 229-246.


Transcriptional regulation is one of the most important means of gene regulation. Uncovering transcriptional regulatory network helps us to understand the complex cellular process. In this paper, we describe a comprehensive statistical approach for constructing the transcriptional regulatory network using data of gene expression, promoter sequence, and transcription factor binding sites. Our simulation studies show that the overall and false positive error rates in the estimated transcriptional regulatory network are expected to be small if the systematic noise in the constructed feature matrix is small. Our analysis based on 658 microarray experiments on yeast gene expression programs and 46 transcription factors suggests that the method is capable of identifying important transcriptional regulatory interactions and uncovering the corresponding regulatory network structures.


Microarrays | Statistical Models