A statistical method to incorporate biological knowledge for generating testable novel gene regulatory interactions from microar

Background: The incorporation of prior biological knowledge in the analysis of microarray data has become important in the reconstruction of transcription regulatory networks in a cell. Most of the current research has been focused on the integration of multiple sets of microarray data as well as curated databases for a genome scale reconstruction. However, individual researchers are more interested in the extraction of most useful information from the data of their hypothesis-driven microarray experiments. How to compile the prior biological knowledge from literature to facilitate new hypothesis generation from a microarray experiment is the focus of this work. We propose a novel method based on the statistical analysis of reported gene interactions in PubMed literature. Results: Using Gene Ontology (GO) Molecular Function annotation for reported gene regulatory interactions in PubMed literature, a statistical analysis method was proposed for the derivation of a likelihood of interac...

• Type: Journal
• Year: 2007
• Where: BMCBI
• Authors: Peter Larsen, Eyad Almasri, Guanrao Chen, Yang Dai
• Keywords: BMCBI 2007, Gene, Microarray Data, Prior Biological Knowledge