Biological cluster validity indices based on the gene ontology

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 3646 LNCS, Issue , 2005, Pages 429-439

  Speer, Nora ^a   Spieth, Christian ^a   Zell, Andreas ^a  

^a UNIVERSITY OF TÜBINGEN   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; DATABASE SYSTEMS; DNA; PARAMETER ESTIMATION;

BIOLOGICAL RELEVANCE; BIOTECHNOLOGICAL HIGH THROUGHPUT METHODS; DNA MICROARRAYS;

BIOTECHNOLOGY;

EID: 33745184704     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11552253_39     Document Type: Conference Paper

References (25)

1. F. Azuaje. A cluster validity framework for genome expression data. Bioinformatics, 18(2):319-320, 2001.
2. T. Beißbarth and T. Speed. GOstat: find statistically overexpressed Gene Ontologies within groups of genes. Bioinformatics, 20(9):1464-1465, 2004.
3. N. Bolshakova, F. Azuaje, and P. Cunningham. An integrated tool for microarray data clustering and cluster validity assessment. Bioinformatics, 21(4):451-455, 2004.
4. R.J. Cho, M. Huang, M.J. Campbell, H. Dong, L. Steinmetz, L. Sapinoso, G. Hampton, S.J. Elledge, R.W. Davis, and D.J. Lockhart. Transcriptional regulation and function during the human cell cycle. Nature Genetics, 27(1):48-54, 2001.
5. J.L. Davies and D.W. Bouldin. A cluster separation measure. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1:224-227, 1979.
6. J.C. Dunn. Well separated clusters and optimal fuzzy partitions. Journal of Cybernetics, 4:95-104, 1974.
7. M. Eisen, P. Spellman, D. Botstein, and P. Brown, Cluster analysis and display of genome-wide expression patterns. In Proceedings of the National Academy of Sciences, USA, volume 95, pages 14863-14867, 1998.
8. Gene Lynx, http://www.genelynx.org, 2004.
9. T.R. Hvidsten, A. Laegreid, and J. Komorowski. Learning rule-based models of biological process From gene expression time profiles using Gene Ontology. Bioinformatics, 19(9):1116-1123, 2003.
10. V.R. Iyer, M.B. Eisen, D.T. Ross, G. Schuler, T. Moore, J.C.F. Lee, J.M. Trent, L.M. Staudt, J Hudson Jr, M.S. Boguski, D. Lashkari, D. Shalon, D. Botstein, and P.O. Brown, The transcriptional program in response of human fibroblasts to serum. Science, 283:83-87, 1999.
11. J.J. Jiang and D.W. Conrath. Semantic similarity based on corpus statistics and lexical taxonomy. In Proceedings of the International Conference on Research in Computational Linguistics, Taiwan, 1998. ROCLING X.
12. P.W. Lord, R.D. Stevens, A. Brass, and C.A. Goble. Semantic similarity measures as tools for exploring the gene ontology. In Proceedings of the Pacific Symposium on Biocomputing, pages 601-612, 2003.
13. Peter Merz. Clustering gene expression profiles with memetic algorithms. In Proceedings of the 7th International Conference on Parallel Problem Solving from Nature, PPSN VII, pages 811-820. Lecture Notes in Computer Science, Springer, Berlin, Heidelberg, 2002.
14. J. Quackenbush. Computational analysis of microarray data. Nature Reviews Genetics, 2(6):418-427, 2001.
15. W. M. Rand. Objective criteria for the evaluation of clustering methods. Journal of the American Statistical Association, 66:846-850, 1971.
16. S. Raychaudhuri and R.B. Altman. A literature-based method for assessing the functional coherence of a gene group. Bioinformatics, 19(3):396-401, 2003.
17. P.N Robinson, A. Wollstein, U. Böhme, and B. Beattie. Ontologizing gene-expression microarray data: characterizing clusters with gene ontology. Bioinformatics, 20(6):979-981, 2003.
18. P. J. Rousseeuw. Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. Journal of Computational Applications in Math, 20:53-65, 1987.
19. N.H. Shah and N.V. Fedoroff. CLENCH: a program for calculating Cluster ENriCHment using Gene Ontology. Bioinformatics, 20(7):1196-1197, 2004.
20. N. Speer, P. Merz, C. Spieth, and A. Zell. Clustering gene expression data with memetic algorithms based on minimum spanning trees. In Proceedings of the IEEE Congress on Evolutionary Computation (CEC 2005), volume 3, pages 1848-1855. IEEE Press, 2003.
21. N. Speer, C. Spieth, and A. Zell. A memetic clustering algorithm for the functional partition of genes based on the Gene Ontology. In Proceedings of the 2004 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB 2004), Pages 252-259. IEEE Press, 2004.
22. P. Tamayo, D. Slonim, J. Mesirov, Q. Zhu, S. Kitareewan, E. Dmitrovsky, E.S. Lander, and T.R. Golub. Interpreting patterns of gene expression with self-organizing maps: Methods and application to hematopoietic differentiation. In Proceedings of the National Academy of Sciences, USA, volume 96, pages 2907-2912, 1999.
23. S. Tavazoie, J.D. Hughes, M.J. Campbell, R.J. Cho, and G.M. Church. Systematic determination of genetic network architecture. Nature Genetics, 22:281-285, 1999.
24. The Gene Ontology Consortium. The gene ontology (GO) database and informatics resource. Nucleic Acids Research, 32:D258-D261, 2004.
25. K.Y. Yeung, D.R. Haynor, and W.L. Ruzzo. Validating clustering for gene expression data. Bioinformatics, 17:309-318, 2001.