SEGS: Search for enriched gene sets in microarray data
Journal of Biomedical Informatics
Interpreting Gene Expression Data by Searching for Enriched Gene Sets
AIME '07 Proceedings of the 11th conference on Artificial Intelligence in Medicine
Gene Ontology Assisted Exploratory Microarray Clustering and Its Application to Cancer
PRIB '08 Proceedings of the Third IAPR International Conference on Pattern Recognition in Bioinformatics
Formulating and testing hypotheses in functional genomics
Artificial Intelligence in Medicine
Efficient generation of biologically relevant enriched gene sets
ISBRA'07 Proceedings of the 3rd international conference on Bioinformatics research and applications
Validating gene clusterings by selecting informative gene ontology terms with mutual information
BSB'07 Proceedings of the 2nd Brazilian conference on Advances in bioinformatics and computational biology
TreeHugger: A New Test for Enrichment of Gene Ontology Terms
INFORMS Journal on Computing
Using Gene Ontology annotations in exploratory microarray clustering to understand cancer etiology
Pattern Recognition Letters
Independent component analysis: Mining microarray data for fundamental human gene expression modules
Journal of Biomedical Informatics
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
AOW '07 Proceedings of the Third Australasian Workshop on Advances in Ontologies - Volume 85
Hi-index | 3.84 |
Motivation: The result of a typical microarray experiment is a long list of genes with corresponding expression measurements. This list is only the starting point for a meaningful biological interpretation. Modern methods identify relevant biological processes or functions from gene expression data by scoring the statistical significance of predefined functional gene groups, e.g. based on Gene Ontology (GO). We develop methods that increase the explanatory power of this approach by integrating knowledge about relationships between the GO terms into the calculation of the statistical significance. Results: We present two novel algorithms that improve GO group scoring using the underlying GO graph topology. The algorithms are evaluated on real and simulated gene expression data. We show that both methods eliminate local dependencies between GO terms and point to relevant areas in the GO graph that remain undetected with state-of-the-art algorithms for scoring functional terms. A simulation study demonstrates that the new methods exhibit a higher level of detecting relevant biological terms than competing methods. Availability: topgo.bioinf.mpi-inf.mpg.de Contact: alexa@mpi-sb.mpg.de Supplementary Information: Supplementary data are available at Bioinformatics online.