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- W1754921003 abstract "In the last years, the completion of the human genome sequencing showed up 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 the patterns within the available sequences.This paper gives a contribution in this setting by providing some algorithms which allow to discover frequent structured patterns, either in exact or approximate form, present in a collection of input biological sequences." @default.
- W1754921003 created "2016-06-24" @default.
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- W1754921003 date "2002-01-01" @default.
- W1754921003 modified "2023-09-27" @default.
- W1754921003 title "Discovering Frequent Structured Patterns from String Databases: An Application to Biological Sequences" @default.
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- W1754921003 doi "https://doi.org/10.1007/3-540-36182-0_6" @default.
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