The post-genomic era showed up a wide range of new challenging issues for the areas of knowledge discovery and intelligent information management. Among them, the discovery of complex pattern repetitions in string databases plays an important role, specifically in those contexts where even what are to be considered the interesting pattern classes is unknown. This work provides a contribution in this precise setting, proposing a novel approach, with several advanced features, for discovering interesting pattern classes from a given data set. Specifically, L-SME provides a graphical, intuitive and flexible framework allowing the user to look for a wide range of pattern classes appearing in a set of biological sequences.
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