Seeing the forest for the trees: Using the Gene Ontology to restructure hierarchical clustering

Bioinformatics

Volumn 25, Issue 14, 2009, Pages 1789-1795

  Dotan Cohen, Dikla ^a   Kasif, Simon ^b,c,d   Melkman, Avraham A ^a  

^a BEN GURION UNIVERSITY OF THE NEGEV   (Israel)

^b Boston University   (United States)

^c BOSTON UNIVERSITY   (United States)

^d HARVARD MIT DIVISION OF HEALTH SCIENCES AND TECHNOLOGY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; BREAST CANCER; CANCER GENETICS; CLUSTER ANALYSIS; GENE EXPRESSION; GENETIC ALGORITHM; GLUCOSE METABOLISM; HUMAN; INTERMETHOD COMPARISON; PHYLOGENETIC TREE; PRIORITY JOURNAL; PROTEIN PROTEIN INTERACTION; TUMOR IMMUNITY;

ALGORITHMS; CLUSTER ANALYSIS; COMPUTATIONAL BIOLOGY; DATABASES, PROTEIN; GENE EXPRESSION PROFILING; HUMANS; PROTEINS;

EID: 67649840899     PISSN: 13674803     EISSN: 14602059     Source Type: Journal    
DOI: 10.1093/bioinformatics/btp327     Document Type: Article

References (29)

1. Bellazzi,R., and Zupan,B (2007) Towards knowledge-based gene expression data mining. J. Biomed. Inform., 6, 787-802.
2. Buehler,E.C. et al. (2004) The CRASSS plug-in for integrating annotation data with hierarchical clustering results. Bioinformatics 20, 3266-3269.
3. Cheng,J. et al. (2004) A knowledge-based clustering algorithm driven by Gene Ontology. J. Biopharm. Stat., 14, 687-700.
4. Crocker,P.R. et al. (2007) Siglecs and their roles in the immune system. Nat. Rev. Immunol., 7, 255-266.
5. Curtis,R.K. et al. (2005) Pathways to the analysis of microarray data. Trends Biotechnol., 23, 429-435.
6. Doherty,J.M. et al. GOurmet: A tool for quantitative comparison and visualization of gene expression profiles based on gene ontology (GO) distributions. BMC Bioinformatics, 7, 151.
7. Dotan-Cohen,D. et al. (2007) Hierarchical tree snipping: clustering guided by prior knowledge. Bioinformatics, 23, 3335-3342.
8. Fang,Z. et al. (2006) Knowledge guided analysis of microarray data. J. Biomed. Inform., 39, 401-411.
9. Gatenby,R.A. and Gillies,R.J. (2004) Why do cancers have high aerobic glycolysis? Nat. Rev. Cancer, 4, 891-899.
10. Hanisch,D. et al.. (2002) Co-clustering of biological networks and gene expression data. Bioinformatics, 18, 145-154.
11. Huang,D. and Pan,W. (2006) Incorporating biological knowledge into distance-based clustering analysis of microarray gene expression data. Bioinformatics, 22, 1259-1268.
12. Jiang,J.J. and Conrath,D.W. (1997) Semantic similarity based on corpus statistics and lexical taxonomy. In Procedings of the International Conference on Research in Computational Linguistics, ROCLING X, Taiwan.
13. Khatri,P., and Draghici,S. (2005) Ontological analysis of gene expression data: Current tools, limitations, and open problems. Bioinformatics, 21, 3587-3595.
14. Kustra,R. et al. (2006) A factor analysis model for functional genomics. BMC Bioinformatics, 7, 216.
15. Kustra,R. and Zagdanski,A. (2007) Data-fusion in clustering microarray data: Balancing discovery and interpretability. IEEE/ACM Trans. Comput. Biol. Bioinform., 1, 1.
16. Lin,D. (1998) An information-theoretic definition of similarity. In Proceedings of the 15th International Conference on Machine Learning. Morgan Kaufmann, San Francisco, CA, pp. 296-304.
17. Lord,P.W. et al. (2003) Investigating semantic similarity measures across the Gene Ontology: The relationship between sequence and annotation. Bioinformatics, 19, 1275-1283.
18. Qi,Z.M. et al. (2005) Polymorphism of the mouse gene for the interleukin 10 receptor alpha chain (Il10ra) and its association with the autoimmune phenotype. Immunogenetics, 57, 697-702.
19. Resnik,P. (1999) Semantic similarity in a taxonomy: An information-based measure and its application to problems of ambiguity in natural language. J. Artif. Intell. Res., 11, 95-130.
20. Schlicker,A. et al.. (2006) A new measure for functional similarity of gene products based on Gene Ontology. BMC Bioinformatics, 7, 302.
21. Simmons,D. and Seed,B. (1988) Isolation of a cDNA encoding CD33, a differentiation antigen of myeloid progenitor cells. J. Immunol., 141, 2797-2800.
22. Speer,N. et al. (2004) A memetic co-clustering algorithm for gene expression profiles and biological annotation. CIBCB, 2, 1631-1638.
23. Spellman,P.T. et al. (1998) Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization. Mol. Biol. Cell., 9, 3273-3297.
24. Teschendorff,A.E. et al. (2007) An immune response gene expression module identifies a good prognosis subtype in estrogen receptor negative breast cancer. Genome Biol., 8, R157.
25. Toronen,P. (2004). Selection of informative clusters from hierarchical cluster tree with gene classes. BMC Bioinformatics, 5, 32.
26. van't Veer,L.J. et al. (2002) Gene expression profiling predicts clinical outcome of breast cancer. Nature, 415, 530-536.
27. Wang,H. et al. (2004) Gene expression correlation and Gene Ontology-based similarity: An assessment of quantitative relationships. In Proceedings of the 2004 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB 2004) San Diego, CA, pp. 25-31.
28. Weber,R.E. et al. (2004) Modulation of red cell glycolysis: interactions between vertebrate hemoglobins and cytoplasmic domains of band 3 red cell membrane proteins. Am. J. Physiol. Regul. Integr. Comp Physiol., 287, 454-464.
29. Yona,G. et al. (2007) Comparing algorithms for clustering of expression data - how to assess gene clusters. In Computational Systems Biology. Humana press, Totowa, NJ.