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Review of the Extraction Methods of DNA Microarray Features Based on Central Decision Class Separation vs Rough Set Classifier

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The study of DNA microarray gene extraction methods is an important and current area of research. Many researchers study gene ontological character, which contain significant information about symptoms of illnesses in tissues, types of organisms, and the distinguishing of some organisms' features. DNA microarray gene extraction methods allow us to choose the most significant genes for a given problem and some ways of their extraction. In this article, we aim to compare three methods of gene extraction. The first and second types are based on, respectively, the modified Fisher and F statistics methods. The last one is based on the novel experimental statistics called A. A common element of those three methods is the way in which we choose genes after the calculation of decision classes' separation ratio. Additionally, all three algorithms are based on the idea of central class separation from other decision concepts. We use our best 8v1.4 granular weighted voting classier as the basic element of comparison of our gene selection methods. The results of the research show that A statistics are better than other methods in all cases. In this article the best one is the SAM10 method, which works well for a small number of genes - less than one hundred. For a higher number of separated genes the SAM5 method is better - its effectiveness has been proven in recent published works.
Rocznik
Strony
241--254
Opis fizyczny
Bibliogr. 24 poz., tab.
Twórcy
autor
  • Department of Mathematics and Computer Science, University of Warmia and Mazury, Olsztyn,Poland
Bibliografia
  • [1] Artiemjew P.: The Extraction Method of DNA Microarray Features Based on Experimental A Statistics. In: J.T. Yao et al. (eds.) RSKT 2011, Banff, Canada, LNCS, Springer, Heidelberg, vol. 6954, 2011, 642-648.
  • [2] Artiemjew P.: The Extraction Method of DNA Microarray Features Based on Modified F Statistics vs Classifier Based on Rough Mereology. In: Kryszkiewicz, M., Rybinski, H., Skowron, A., Ras, Z., W. (eds.) ISMIS 2011, Warsaw, Poland, LNAI, Springer, Heidelberg, vol. 6804, 2011, 33-42.
  • [3] Artiemjew P.: Classifiers based on rough mereology in analysis of DNA microarray data, in: Proceedings 2010 IEEE International Conference on Soft Computing andPattern Recognition SocPar’10, Sergy Pontoise France, IEEE Press, 2010.
  • [4] Artiemjew P.: On strategies of knowledge granulation and applications to decision systems, PhD Dissertation, Polish Japanese institute of Information Technology, L. Polkowski, Supervisor, Warsaw, 2009.
  • [5] Artiemjew P.: Rough mereological classifiers obtained from weak rough set inclusions, in: Proceedings of Int. Conference on Rough Set and Knowledge Technology RSKT’08, Chengdu China, LNAI, Springer Verlag, Berlin, vol. 5009, 2008, 229-236.
  • [6] Artiemjew P.: On classification of data by means of rough mereological granules of objects and rules, in: Proceedings of Int. Conference on Rough Set and Knowledge Technology RSKT’08, Chengdu China, LNAI, Springer Verlag, Berlin, vol. 5009, 2008, 221-228.
  • [7] Artiemjew P.: Natural versus granular computing: Classifiers from granular structures, in: Proceedings of 6th International Conference on Rough Sets and Current Trends in Computing RSCTC’08, Akron, Ohio, USA, Springer Berlin / Heidelberg, vol. 5306, 2008, 150-159.
  • [8] Artiemjew P.: Classifiers from granulated data sets: Concept dependent and layered granulation, in: Proceedings RSKD’07. Workshop at ECML/PKDD’07, Warsaw Univ. Press, Warsaw, 2007, 1-9.
  • [9] Brown M., Grundy W., et al.: Knowledge-based analysis of microarray gene expression data by using support vector machines, University of California, 1999.
  • [10] Eisen MB, Brown PO: DNA arrays for analysis of gene expression. Methods Enzymol 303, 1999, 179-205.
  • [11] Furey T.S., Cristianini, Duffy N., Bernarski, Schummer M., Haussler D.: ”Support Vector Machine Classification and Validation of Cancer Tissue Samples Using Microarray Expression Data,” Bioinformatics, vol. 16, 2000, 906-914.
  • [12] Hájek P.: Metamathematics of Fuzzy Logic. Kluwer, Dordrecht, 1998.
  • [13] Molinaro A.M., Simon R., Pfeiffer R.M.: Prediction error estimation: a comparison of resampling methods, in: Bioinformatics, vol. 21, issue 15, Oxford UniversityPress, Oxford, UK, 2005, 3301-3307.
  • [14] Polkowski L.: Toward rough set foundations. Mereological approach (a plenary lecture), in: Proceedings RSCTC04, Uppsala, Sweden, 2004, LNAI, Springer Verlag, Berlin, vol. 3066, 2004, 8-25.
  • [15] Polkowski L.: Formal granular calculi based on rough inclusions (a feature talk), in: Proceedings 2005 IEEE Int. Confrence on Granular Computing GrC’05, IEEE Press, 2005, 57-62.
  • [16] Polkowski L.: Formal granular calculi based on rough inclusions (a feature talk), in: Proceedings 2006 IEEE Int. Conference on Granular Computing GrC’06, IEEE Press, 2006, 57-62.
  • [17] Polkowski L.: Granulation of knowledge in decision systems: The approach based on rough inclusions. The Method and its applications (plenary talk), in: Lecture Notes in Artificial Intelligence (Proceedings RSEiSP’07), Springer Verlag, Berlin, vol. 4585, 2005, 69-79.
  • [18] Polkowski L.: The paradigm of granular rough computing, in: ProceedingsICCI’07. 6th IEEE Intern. Conf. on Cognitive Informatics, IEEE Computer Society, Los Alamitos, CA, 2007, 145-163.
  • [19] Polkowski L.: A Unified Approach to Granulation of Knowledge and Granular Computing Based on Rough Mereology: A Survey, in: Handbook of Granular Computing, Witold Pedrycz, Andrzej Skowron, Vladik Kreinovich (Eds.), John Wiley & Sons, New York, 2008, 375-401.
  • [20] Polkowski L., Artiemjew P.: On classifying mappings induced by granular structures. Transactions on Rough Sets IX. Lecture Notes in Computer Science, Springer Verlag, Berlin, vol. 5390, 2008, 264-286.
  • [21] Polkowski L., Artiemjew P.: A study in granular computing: On classifiers induced from granular reflections of data, Transactions on Rough Sets IX. Lecture Notes in Computer Science, Springer Verlag, Berlin, vol. 5390,2008, 230-263.
  • [22] Schena M.: Microarray analysis. Wiley, Hoboken, NJ, USA, 2003.
  • [23] http://tunedit.org/repo/RSCTC/2010/A
  • [24] Zadeh L.A.: Fuzzy sets and information granularity. In: Gupta, M., Ragade, R., Yager, R.R.(Eds.): Advances in Fuzzy Set Theory and Applications. North Holland, Amsterdam, 1979, 3-18.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-d170dd88-a6f2-45b4-a47a-aef3cfa1d51a
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