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A Fast Technique For Deriving Frequent Structured Patterns From Biological Data Sets

Author

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  • GIORGIO TERRACINA

    (Dipartimento di Matematica, Università della Calabria, Via P. Bucci 87036 Rende (CS), Italy)

Abstract

In the last years, the completion of the human genome sequencing showed a wide range of new challenging issues involving raw data analysis. In particular, the discovery of information implicitly encoded in biological sequences is assuming a prominent role in identifying genetic diseases and in deciphering biological mechanisms. This information is usually represented by patterns frequently occurring in the sequences. Because of biological observations, a specific class of patterns is becoming particularly interesting:frequent structured patterns. In this respect, it is biologically meaningful to look at both "exact" and "approximate" repetitions of pattens within the available sequences. This paper gives a contribution in this setting by providing algorithms which allow to discover frequent structured patterns, both in "exact" and "approximate" form, present in a collection of input biological sequences.

Suggested Citation

  • Giorgio Terracina, 2005. "A Fast Technique For Deriving Frequent Structured Patterns From Biological Data Sets," New Mathematics and Natural Computation (NMNC), World Scientific Publishing Co. Pte. Ltd., vol. 1(02), pages 305-327.
    • Handle: RePEc:wsi:nmncxx:v:01:y:2005:i:02:n:s1793005705000111
    DOI: 10.1142/S1793005705000111
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