PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Powiadomienia systemowe
  • Sesja wygasła!
Tytuł artykułu

Analysis of GO composition of gene clusters by using multiattribute decision rules

Autorzy
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In this paper, a novel method for characterizing the Gene Ontology (GO) composition of the gene clusters on basis of the decision rules is presented. The rules are expressed as logical functions of the Gene Ontology terms which are interpreted as binary attributes. A new method for evaluating the quality of decision rules based on statistical significance is developed. The presented approach is applied to the well-known data set and the results are compared with the results obtained by other authors.
Twórcy
autor
Bibliografia
  • 1. Baldi P., Hatfield G.W.: DNA Microarrays and Gene Expression. Cambridge University Press, Cambridge 2002.
  • 2. Speed T. (ed.): Statistical Analysis of Gene Expression Micoroarray Data. Interdisciplinary Statistics. Chapman & Hall/CRC, Boca Ration 2003.
  • 3. Allison D.B., Gadbury G., Heo M., Fernandez J., Lee C.K., Prolla T.A., Weindruch R.: A Mixture Model Approach for the Analysis of Microarray Gene Expression Data. Comput. Statist. Data Anal., 2002, 39, 1-20.
  • 4. Storey J.D., Tibshirani R.: Statistical Significance for Genomewide Studies. Proc. Natl. Acad. Sci. USA, 2003, 100, 9440-9445.
  • 5. Alizadeh A.A., Eisen M.B., Davis R.E., Ma C., Lossos I.S., Rosenwald A., Boldrick J.C., Sabet H., Tran T., Yu X. et al.: Distinct Types of Diffuse Large B-Cell Lymphoma Identified by Gene Expression Profiling. Nature, 2000, 403, 503-511.
  • 6. Beer D.G., Kardia S.L.R., Huang C.C., Giordano T.J., Levin A.M., Misek D.E., Lin L., Chen G.A., Gharib T.G. et al.: Gene Expression Profiles Predict Survival of Patients with Lung Adenocarcinoma. Nat. Med., 2002, 8, 816-824.
  • 7. Cho R.J., Campbell M.J., Winzeler E.A., Steinmetz L., Conway A., Wodicka L., Wolfsberg T.G., Gabrielian A.E., Landsman D., Lockhart D.J., Davis R.W.: A Genome-Wide Transcriptional Analysis of the Mitotic Cell Cycle. Mol. Cell., 1998, 2, 65-73.
  • 8. Golub T.R., Slonim D.K., Tamayo P., Huard C., Gaasenbeek M., Mesirov J.P., Coller H., Loh M.L., Downing J.R., Caligiuri M.A., Bloomfield C.D., Lander E.: Molecular Classification of Cancer Class Discovery and Class Prediction by Gene Expression Monitoring. Science, 1999, 286, 531-537.
  • 9. Jarząb B., Wiench M., Fujarewicz K., Simek K., Jarząb M., Oczko-Wojciechowska M., Wloch J., Czarniecka A., Chmielik E., Lange D., Pawlaczek A., Szpak S., Gubala E., Siwerniak A.: Gene Expression Profile of Papillary Thyroid Cancer: Sources of Variability and Diagnostic Implications. Cancer Res., 2005, 65, 1587-1597.
  • 10. Rhodes D.R., Yu J., Shanker K., Deshpande N., Varambally R., Ghosh D., Barrette T., Pandey A., Chinnaiyan A.M.: ONCOMINE, A Cancer Microarray Database and Integrated Data Mining Platform. Neoplasia, 2004, 6, 1-6.
  • 11. Ashburner M., Ball C.A., Blake J.A., Botstein D., Butler H., Cherry J.M. et al.: Gene Ontology: Tool for the Unification of Biology. The Gene Ontology Consortium. Nature genetics, 2000, 25, 25-29.
  • 12. Al-Shahrour F., Minguez P., Vaquerizas J.M., Conde L., Dopazo J.: BABELOMICS: A Suite of Web Tools for Functional Annotation and Analysis of Groups of Genes in High-Throughput Experiments. Nucleic Acid Research, 2005, 33, W460-W464.
  • 13. Subramanian A., Tamayo P., Mootha V.K., Mukherjee S., Ebert B.L., Gillette M.A. et al.: Gene Set Enrichment Analysis: A Knowledge-Based Approach for Interpreting Genome-Wide Expression Profiles. Proc. Natl. Acad. Sci. USA, 2005, 102, 15545-15550.
  • 14. Svensson J.P., Stalpers L.J., Esveldt-van Lange R.E., Franken N.A., Haveman J., Klein B., Turesson I., Vrieling H. and Giphart-Gassler M.: Analysis of Gene Expression Using Gene Sets Discriminates Cancer Patients with and without Late Radiation Toxicity, PLOS Med., 2006, 3, 1905-1914.
  • 15. Lee I.Y., Ho J.M., Chen M.S.: CLUGO: A Clustering Algorithm for Automated Functional Annotations Based on Gene Ontology. Proc. of the Fifth IEEE Int. Conf. on Data Mining, Houston, Texas, 2005, 705-708.
  • 16. Lee S.G., Hur J.U., Kim Y.S.: A Graph-Theoretic Modeling on GO Space for Biological Interpretation of Gene Clusters. Bioinformatics, 2004, 20, 381-388.
  • 17. Midelfart H., Komorowski H.J.: A Rough Set Framework for Learning in a Directed Acyclic Graph. Rough Sets and Current Trends in Computing, 2002, 144-155.
  • 18. Wang H., Azuaje F., Bodenreider O., Dopazo J.: Gene Expression Correlation and Gene Ontology-Based Similarity: an Assessment of Quantitative Relationships. Proc of the 2004 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, La Jolla, California, 2004, 25-31.
  • 19. Pawlak Z.: Rough Sets: Theoretical aspects of reasoning about data. Kluwer Academic Publisher, Dordrecht 1991.
  • 20. Eisen M.B., Spellman P.T., Brown P.O., Botstein D.: Cluster analysis and display of genome-wide expression patterns. Proc. Natl. Acad. Sci. USA, 1998, 95, 14863-14868.
  • 21. Skowron A., Rauszer C.: The Discernibility Matrices and Functions in Information Systems, in: Słowinski, R. (Ed.), Intelligent Decision Support: Handbook of Applications and Advances to Rough Sets Theory, Kluwer Academic Publisher, Dordrecht 1992, 331-362.
  • 22. Nguyen S.H., Nguyen H.S.: Some Efficient Algorithms for Rough Set Methods. Proc. of the Conference of Information Processing and Management of Uncertainty in Knowledge-Based Systems, Granada, Spain, 1996, 1451-145.
  • 23. Gruca A.: A Rule-Based Characterization of Gene Clusters. Proc. of the International Multiconference on Computer Science and Information Technology, Wisla, Poland, 2007, 2, 93-102.
  • 24. Mitchell T.: Machine Learning. McGraw-Hill, New York 1997.
  • 25. Bruha I.: Quality of Decision Rules: Definitions and Classification Schemes for Multiple Rules, in: Nakhaeizadeh G., Taylor C.C. (Eds.), Machine Learning and Statistics, The Interface, John Wiley and Sons 1997.
  • 26. Rice J.A.: Mathematical Statistics and Data Analysis. 2nd edn. Duxbury Press, Belmont 1995.
  • 27. Kikyo M. et al.: An FH domain-containing Bnr1p is a multifunctional protein interacting with a variety of cytoskeletal proteins in Saccharomyces cerevisiae, Oncogene, 1999, 18, 7046-7054.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BPZ1-0048-0002
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.