Finding Minimal Multiple Generalization over Regular Patterns with Alphabet Indexing

Abstract

We propose a learning algorithm that discovers a motif represented by patterns and an alphabet indexing from biosequences. From only positive examples with the help of an alphabet indexing, the algorithm finds k regular patterns as a k-minimal multiple generalization (k-mmg for short). The computational results for transmembrane domains indicate that the combination of k-mmg and alphabet indexing works quite successful. We also introduce a partial alphabet indexing that transforms symbols dependently on the position in sequences.