Quality improvement of rule-based gene group descriptions using information about go terms importance occurring in premises of determined rules

International Journal of Applied Mathematics and Computer Science

Volumn 20, Issue 3, 2010, Pages 555-570

  Sikora, Marek ^a,b   Gruca, Aleksandra ^a  

^a SILESIAN UNIVERSITY OF TECHNOLOGY   (Poland)

^b Institute of Innovative Technologies EMAG   (Poland)

Author keywords

decision rules; descriptions of gene groups; gene ontology; importance of rules premises; measures of rules interestingness

Indexed keywords

DECISION RULES; DESCRIPTIONS OF GENE GROUPS; GENE ONTOLOGY; IMPORTANCE OF RULES PREMISES; INTERESTINGNESS;

ONTOLOGY;

GENES;

EID: 77957594096     PISSN: 1641876X     EISSN: None     Source Type: Journal    
DOI: 10.2478/v10006-010-0041-3     Document Type: Article

References (38)

1. Agrawal, R. and Srikant, R. (1994). Fast algorithms for mining association rules, VLDB'94, Proceedings of the 20th International Conference on Very Large Data Bases, Santiago de Chile, Chile, pp. 487-499.
2. Agresti, A. (2002). Categorical Data Analysis, Wiley Interscience, Hoboken, NJ.
3. Al-Shahrour, F., Minguez, P., Vaquerizas, J., Conde, L. and Dopazo, J. (2005). Babelomics: A suite of web tools for functional annotation and analysis of groups of genes in high-throughput experiments, Nucleic Acids Research 33: W460-W464.
4. An, A. and Cercone, N. (2001). Rule quality measures for rule induction systems: Description and evaluation, Computational Intelligence 17(3): 409-424. (Pubitemid 32934013)
5. Ashburner, M., Ball, C., Blake, J., Botstein, D., Butler, H., Cherry, J., Davis, A., Dolinski, K., Dwight, S., Eppig, J., Harris, M., Hill, D. P., Issel-Tarver, L., Kasarskis, A., Lewis, S., Matese, J., Richardson, J., Ringwald, M., Rubin, G. and Sherlock, G. (2000). Gene ontology: Tool for the unification of biology, Nature Genetics 25(1): 25-29. (Pubitemid 30257031) (Pubitemid 30257031)
6. Bairagi, R. and Suchindran, C. (1989). An estimator of the cutoff point maximizing sum of sensitivity and specificity, Sankhya, Indian Journal of Statistics 51(B-2): 263-269.
7. Baldi, P. and Hatfield, G. (2002). DNA Microarrays and Gene Expression, Cambridge University Press, Cambridge.
8. Banzhaf, J. (1965). Weighted voting doesn't work: A mathematical analysis, Rutgers Law Review 19(2): 317-343.
9. Benjamini, Y. and Hochberg, T. (1995). Controlling the false discovery rate: A practical and powerful approach to multiple testing, Journal of the Royal Statistical Society: Series B 57(1): 289-300.
10. Bruckmann, A., Hensbergen, P., Balog, C., Deelder, A., de Steensma, H. and van Heusden, G. (2007). Posttranscriptional control of the saccharomyces cerevisiae proteome by 14-3-3 proteins, Journal of Proteome Research 6(5): 1689-1699. (Pubitemid 46814493) (Pubitemid 46814493)
11. Brzezinska, I., Greco, S. and Slowinski, R. (2007). Mining pareto-optimal rules with respect to support and confirmation or support and anti-support, Engineering Applications of Artificial Intelligence 20(5): 587-600. (Pubitemid 46856151)
12. Carmona-Saez, P., Chagoyen, M., Rodriguez, A., Trelles, O., Carazo, J. and Pascual-Montano, A. (2006). Integrated analysis of gene expression by association rules discovery, BMC Bioinformatics 7(1): 54. (Pubitemid 43323564) (Pubitemid 43323564)
13. Carmona-Saez, P., Chagoyen, M., Tirado, F., Carazo, J. and Pascual-Montano, A. (2007). Genecodis: A web-based tool for finding significant concurrent annotations in gene lists, Genome Biology 8(1): R3.
14. Eisen, M., Spellman, P., Brown, P. and Botstein, D. (1998). Cluster analysis and display of genome-wide expression patterns, Proceedings of the National Academy of Sciences of the United States of America 95(25): 14863-14868. (Pubitemid 29003722) (Pubitemid 29003722)
15. Fayyad, U., Piatetsky-Shapiro, G. and Smyth, P. (1996). From data mining to knowledge discovery: An overview, in U. Fayyad, G. Piatetsky-Shapiro, P. Smyth and R. Uthurusamy (Eds.), Advances in Knowledge Discovery and Data Mining, American Association for Artificial Intelligence, Menlo Park, CA, pp. 1-34.
16. Fürnkranz, J. (1999). Separate-and-conquer rule learning, Artificial Intelligence Review 13(1): 3-54.
17. Fürnkranz, J. and Flach, P. (2005). Roc'n' rule learning-Towards a better understanding of covering algorithms, Machine Learning 58(1): 39-77. (Pubitemid 40356739) (Pubitemid 40356739)
18. Greco, S., Pawlak, Z. and Słowiński, R. (2004). Can Bayesian confirmation measures be useful for rough set decision rules?, Engineering Applications of Artificial Intelligence 17(4): 345-361.
19. Greco, S., Słowiński, R. and Stefanowski, J. (2007). Evaluating importance of conditions in the set of discovered rules, RSFDGrC '07: Proceedings of the 11th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing, Toronto, Ontario, Canada, pp. 314-321.
20. Gruca, A. and Sikora, M. (2009). Ontological description of gene groups by the multiattribute statistically significant logical rules, in S. Safeeullah (Ed.), Engineering the Computer Science and IT, INTECH, Vukovar, pp. 277-303.
21. Gruca, A., Sikora, M., Chróst, ł. and Polański, A. (2009). Rulego. Bioinformatical internet service system architecture, Proceedings of the 16th Conference on Computer Networks. Communications in Computer and Information Sciences, Wisła, Poland, pp. 160-167.
22. Grzymała-Busse, J., Stefanowski, J. and Wilk, S. (2005). A comparison of two approaches to data mining from imbalanced data, Journal of Intelligent Manufacturing 16(6): 565-573. (Pubitemid 41605674) (Pubitemid 41605674)
23. Grzymała-Busse, J. and Ziarko, W. (2003). Data mining based on rough sets, in J. Wang (Ed.), Data Mining: Opportunities and Challenges, IGI Publishing, Hershey, PA, pp. 142-173.
24. Guillet, F. and Hamilton, H. (2007). Quality Measures in Data Mining (Studies in Computational Intelligence), Springer-Verlag New York, Inc., Secaucus, NJ.
25. Hackenberg, M. and Matthiesen, R. (2008). Annotationmodules: A tool for finding significant combinations of multisource annotations for gene lists, Bioinformatics 24(11): 1386-1393. (Pubitemid 351737145) (Pubitemid 351737145)
26. Hvidsten, T., Legreid, A. and Komorowski, H. (2003). Learning rule-based models of biological process from gene expression time profiles using gene ontology, Bioinformatics 19(9): 1116-1123. (Pubitemid 36790013) (Pubitemid 36790013)
27. Iyer, V., Eisen, M., Ross, D., Schuler, G., Moore, T., Lee, J., Trent, J., Staudt, L., Hudson, J., Boguski, M., Lashkari, D., Shalon, D., Botstein, D. and Brown, P. (1999). The transcriptional program in the response of human fibroblasts to serum, Science 283(5398): 83-87. (Pubitemid 29044852) (Pubitemid 29044852)
28. Kano, M., Morishita, Y., Iwata, C., Iwasaka, S., Watabe, T., Ouchi, Y., Miyazono, K. and Miyazawa, K. (2005). Vegfa and fgf-2 synergistically promote neoangiogenesis through enhancement of endogenous pdgf-b-pdgfrbeta signaling, Journal of Cell Science 118(Pt 16): 3759-3768. (Pubitemid 41309422) (Pubitemid 41309422)
29. Khatri, P. and Dr?ghici, S. (2005). Ontological analysis of gene expression data: Current tools, limitations, and open problems, Bioinformatics 21(18): 3587-3595.
30. Maere, S., Heymans, K. and Kuiper, M. (2005). Bingo: A cytoscape plugin to assess overrepresentation of gene ontology categories in biological networks, Bioinformatics 21(16): 3448-3449. (Pubitemid 41222459) (Pubitemid 41222459)
31. Mata-Greenwood, E., Meyrick, B., Soifer, S., Fineman, J. and Black, S. (2003). Expression of vegf and its receptors flt-1 and flk-1/kdr is altered in lambs with increased pulmonary blood flow and pulmonary hypertension, American Journal of Physiology: Lung Cellular and Molecular Physiology 285(1): L222-L231. (Pubitemid 36736485) (Pubitemid 36736485)
32. Michalski, R., Bratko, I. and Kubar, M. (1998). Machine Learning and Data Mining: Methods and Applications, John Wiley and Sons, New York, NY.
33. Midelfart, H. (2005a). Supervised learning in the gene ontology, Part I: A rough set framework, in J. Peters and A. Skowron (Eds.) Transactions on Rough Sets IV, Lecture Notes in Computer Science, Vol. 3700, Springer, Berlin/Heidelberg, pp. 69-97.
34. Midelfart, H. (2005b). Supervised learning in gene ontology, Part II: A bottom-up algorithm, in J. Peters and A. Skowron (Eds.) Transactions on Rough Sets IV, Lecture Notes in Computer Science, Vol. 3700, Springer, Berlin/Heidelberg, pp. 98-124.
35. Seghezzi, G., Patel, S., Ren, C., Gualandris, A., Pintucci, G., Robbins, E., Shapiro, R., Galloway, A., Rifkin, D. and Mignatti, P. (1998). Fibroblast growth factor-2 (fgf-2) induces vascular endothelial growth factor (vegf) expression in the endothelial cells of forming capillaries: an autocrine mechanism contributing to angiogenesis, The Journal of Cell Biology 141(7): 1659-1673. (Pubitemid 28309179) (Pubitemid 28309179)
36. Sikora, M. (2006). Rule Quality Measures in Creation and Reduction of Data Role Models, Lecture Notes in Artificial Intelligence, Vol. 4259, Springer, Heidelberg, pp. 716-725.
37. Sikora, M. (2010). Decision rules-based data models using TRS and NetTRS-Methods and algorithms, in J., Peters and A. Skowron (Eds.), Transactions on Rough Sets XI, Lecture Notes on Computer Sciences, Vol. 5946, Springer, Berlin/Heidelberg, pp. 130-160.
38. Stefanowski, J. and Vanderpooten, D. (2001). Induction of decision rules in classification and discovery-oriented perspectives, International Journal on Intelligent Systems 16(1): 13-27. (Pubitemid 32089954)