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Rough Sets in Oligonucleotide Microarray Data Analysis

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Rough Sets and Intelligent Systems Paradigms (RSEISP 2007)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4585))

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Abstract

This paper shows attempts of the rough set theory application to the oligonucleotide microarrays data analysis.

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References

  1. Düntsch, I., Gediga, G.: Rough set data analysis: A road to non-invasive knowledge discovery. Methoδos Publishers, Bangor (2000)

    Google Scholar 

  2. http://portalwiedzy.onet.pl/75936,,,,gen,haslo.html

  3. http://www.biologia.pl/slowniczek/ekspresja_genu.php3

  4. Murray, R.K., Granner, D.K., Mayes, P.A., Rodwell, V.W.: Harper’s Illustrated Biochemistry, 26th edn. McGraw-Hill, New York (2003)

    Google Scholar 

  5. http://www.affymetrix.com

  6. Ao, S.I., Ng, M.K.: Gene expression Time Series Modeling with Principal Component Analysis and Neural Network (published online 10 May 2005). In: Soft Comput., vol. 10, pp. 351–358. Springer, Heidelberg (2006)

    Google Scholar 

  7. Wee-chung Liew, A., Keung Szeto, L., Tang, S.: A Computational Approach to Gene Expression Data Extraction and Analysis. Journal of VLSI Signal Processing 38, 237–258 (2004)

    Article  Google Scholar 

  8. Yu, H., Gao, L., Tu, K., Gou, Z.: Broadly predicting specific gene functions with expression similarity and taxonomy similarity. Gene 352, 75–81 (2005)

    Article  Google Scholar 

  9. Shah, S., Kusiak, A.: Cancer Gene Search with Data-mining and Genetic Algorithms. Computers in Biology and Medicine 37, 251–261 (2007)

    Article  Google Scholar 

  10. Lu, Y., Han, J.: Cancer classification using gene expression data. Information Systems 28, 243–268 (2003)

    Article  MATH  Google Scholar 

  11. Jonsson, P., Laurio, K., Lubovac, Z., Olsson, B., Andersson, M.L.: Using Functional annotation to improve clusterings of gene expression patterns. Information Sciences 145, 183–194 (2002)

    Article  MathSciNet  Google Scholar 

  12. Leng, X., Müller, H.G.: Classification using functional Data Analysis for Temporal Gene Expression Data. Bioinformatics 22(1), 68–76 (2006)

    Article  Google Scholar 

  13. Manduchi, E., Grant, G.R., McKenzie, S.E., Overton, G.C., Surrey, S., Stoeckert Jr., C.: Generation of Patterns from Gene Expression Data by Assigning Confidence to Differentially Expressed Genes. Bioinformatics 16(8), 685–698 (2000)

    Article  Google Scholar 

  14. Tan, Y., Shi, L., Hussain, S.M., Xu, J., Tong, W., Frazier, J.M., Wang, C.: Integrating time-course microarray gene expression profiles with cytotoxicity for identification of biomarkers in primary rat hepatocytes exposed to cadmium. Bioinformatics 22(1), 77–87 (2006)

    Article  Google Scholar 

  15. Wilczok, A.: Cytotoxicity of quinoline derivatives and metaloorganic complexes of cobalt and iron analyzed by transcriptional activity of genes associated with proliferation, apoptosis and angiogenesis in human tumor cell culture lines. In: polish: Cytotoksyczność pochodnych chinoliny oraz metaloorganicznych kompleksów kobaltu i żelaza a aktywność transkrypcyjna genów w procesach proliferacji, apoptozy i angiogenezy w hodowlach komórek ludzkich linii nowotworowych. Habilitation 33/2006, p. 192. Wydawnictwo Śla̧skiej Akademii Medycznej, Katowice (2006)

    Google Scholar 

  16. Günther, C.W., Rinderle, S., Reichert, M., van der Aalst, W.M.P.: Using Process Mining to Learn from Process Changes in Evolutionary Systems. Eindhoven University of Technology, Eindhoven (2006)

    Google Scholar 

  17. Cook, J.E., Wolf, A.L.: Discovering models of software processes from event-based data. ACM Transactions on Software Engineering and Methodology 7(3), 215–249 (1998)

    Article  Google Scholar 

  18. Opgen-Rhein, R., Strimmer, K.: Inferring Gene Dependency Networks from Genomic Longitudinal Data: A Functional Data Approach. Statistical Journal 4(1), 53–65 (2006)

    MathSciNet  Google Scholar 

  19. Pawlak, Z.: Rough Sets: Theoretical Aspects of Reasoning about Data, System Theory. In: Pawlak, Z. (ed.) Knowledge Engineering and Problem Solving, vol. 9, Kluwer Academic Publishers, Dordrecht (1991)

    Google Scholar 

  20. Pawlak, Z., Skowron, A.: Rudiments of rough sets. Information Sciences. An International Journal 177(1), 3–27 (2007)

    MATH  MathSciNet  Google Scholar 

  21. Pawlak, Z., Skowron, A.: Rough sets: Some extensions. Information Sciences. An International Journal 177(1), 28–40 (2007)

    MATH  MathSciNet  Google Scholar 

  22. Pawlak, Z., Skowron, A.: Rough sets and Boolean reasoning. Information Sciences. An International Journal 177(1), 41–73 (2007)

    Article  MATH  MathSciNet  Google Scholar 

  23. Pawlak, Z.: Concurrent versus sequential the rough sets perspective. Bulletin of the EATCS (48), 178–190 (1992)

    MATH  Google Scholar 

  24. Skowron, A., Suraj, Z.: Rough sets and concurrency. Bulletin of the Polish Academy of Sciences 41(3), 237–254 (1993)

    MATH  Google Scholar 

  25. Swiniarski, R., Skowron, A.: Rough set methods in feature selection and extraction. Pattern Recognition Letters 24(6), 833–849 (2003)

    Article  MATH  Google Scholar 

  26. Suraj, Z.: Rough set methods for the synthesis and analysis of concurrent processes. In: Polkowski, L., Tsumoto, S., Lin, T.Y. (eds.) Rough Set Methods and Applications (Studies in Fuzziness and Soft Computing 56), pp. 379–488. Physica-Verlag, Heidelberg (2000)

    Google Scholar 

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Marzena Kryszkiewicz James F. Peters Henryk Rybinski Andrzej Skowron

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© 2007 Springer-Verlag Berlin Heidelberg

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Tkacz, M.A. (2007). Rough Sets in Oligonucleotide Microarray Data Analysis. In: Kryszkiewicz, M., Peters, J.F., Rybinski, H., Skowron, A. (eds) Rough Sets and Intelligent Systems Paradigms. RSEISP 2007. Lecture Notes in Computer Science(), vol 4585. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73451-2_47

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  • DOI: https://doi.org/10.1007/978-3-540-73451-2_47

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-73450-5

  • Online ISBN: 978-3-540-73451-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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