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Tytuł artykułu

DNA sequence analysis

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Warianty tytułu
Konferencja
Evolutionary Computation and Global Optimization (10; Krajowa Konferencja Algorytmy Ewolucyjne i Optymalizacja Globalna; 11-13.06.2007; Będlewo, Poland)
Języki publikacji
EN
Abstrakty
EN
This paper presents a brief survey of computational approaches to the DNA sequence analysis. The basic biological background is presented. The various types of algorithms for pattern construction and gene finding are presented with special attention paid to the application of global optimization methods.
Słowa kluczowe
Rocznik
Tom
Strony
35--42
Opis fizyczny
Bibliogr. 12 poz., wykr.
Twórcy
Bibliografia
  • [1] D.A. Benson et al. Genbank. Nucleic Acids Research, 34:D16, 2006.
  • [2] A. Brazma et al. Pattern discovery in biosequences. In ICGI, pages 257-270, 1998.
  • [3] C.B Burge and S. Karlin. Finding the genes in genomic DNA. Curr. Opin. Struct. Biol., 8(3):346-354, 1998.
  • [4] C.G. Elsik et al. Community annotation: procedures, protocols, and supporting tools. Genome Res., 2006.
  • [5] A. Heddad et al. Evolving regular expression-based sequence classifiers for protein nuclear localisation. In G.R. Raidl et al., editor, EvoWorkshops2004, volume 3005 of LNCS, pages 31-40, Coimbra, Portugal, 2004. Springer.
  • [6] L. Hunter, editor. Artificial intelligence and molecular biology. American Association for Artificial Intelligence, Menlo Park, CA, USA, 1993.
  • [7] I. Jonassen. Efficient discovery of conserved patterns using a pattern graph. Computer Applications in the Biosciences, 13(5):509-522, 1997.
  • [8] J. Koza et al. Using programmatic motifs and genetic programming to classify protein sequences as to extracellular and membrane cellular location. In V.W. Porto et al., editor, Evolutionary Programming VII, volume 1447, San Diego, California, USA, 25-27 1998. Springer.
  • [9] C.K. Reddy et al. Refining motifs by improving information content scores using neighborhood profile search. Algorithms for Molecular Biology, 1:23+, 2006.
  • [10] B.J. Ross. The Evolution of Stochastic Regular Motifs for Protein Sequences. New Generation Computing, 20(2):187-213, 2002.
  • [11] Y. Saeys. Feature selection for classification of nucleic acid sequences. PhD thesis, Ghent University, Belgium, 2004.
  • [12] Wikipedia. Genome - wikipedia, the free encyclopedia, 2007. [Online; accessed 26-February-2007].
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-PWA6-0040-0004
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