Knowledge-based analysis of microarrays for the discovery of transcriptional regulation relationships

Background: The large amount of high-throughput genomic data has facilitated the discovery of the regulatory relationships between transcription factors and their target genes. While early methods for discovery of transcriptional regulation relationships from microarray data often focused on the high-throughput experimental data alone, more recent approaches have explored the integration of external knowledge bases of gene interactions. Results: In this work, we develop an algorithm that provides improved performance in the prediction of transcriptional regulatory relationships by supplementing the analysis of microarray data with a new method of integrating information from an existing knowledge base. Using a wellknown dataset of yeast microarrays and the Yeast Proteome Database, a comprehensive collection of known information of yeast genes, we show that knowledge-based predictions demonstrate better sensitivity and specificity in inferring new transcriptional interactions than pred...

• Type: Journal
• Year: 2010
• Where: BMCBI
• Authors: Junhee Seok, Amit Kaushal, Ronald W. Davis, Wenzhong Xiao
• Keywords: BMCBI 2010, Knowledge Bases, Microarray Data, Regulatory Relationships