Identification of yeast transcriptional regulation networks using multivariate random forests
Transcriptional regulation, one of the most complex and intriguing processes in living cells, drives essential downstream cellular processes such as development, proliferation and differentiation. This elaborate control of gene expression is realized by sophisticated transcriptional regulatory networks, that include a diverse repertoire of transcription factors. Here, we study the relationship between gene expression and transcription factor binding in diverse yeast physiological processes.
I will talk about our novel random forest-based method, which relates gene expression (as derived from expression microarrays) to sequence features residing in gene promoters (as derived from DNA motif data) and transcription factor binding to gene promoters (as derived from tiling microarrays). This new method, multivariate random forest (MRF), effectively models multivariate gene expression measurements simultaneously, bypassing the necessity of analyzing the multiple samples separately. We apply MRF to several yeast physiological processes: cell cycle, sporulation, and various stress conditions and have identified many high-order interactions between regulatory sequences that give rise to condition-specific gene expression.
Dr. Xiao is an Assistant Professor at the UCSF Department of Epidemiology &iamp; Biostatistics. She received her bachelor degree in Beijing University and doctorate in Pharmaceutical Chemistry at UC San Francisco in 2004 and her thesis focused on the statistical and bioinformatical issues in preprocessing and differential expression analysis of microarray data. Her research interests are in computational biology and bioinformatics, including developing efficient and effective methodologies for high dimensional genomic and proteomic data.