@article{4144,
  title = {Mining frequent stem patterns from unaligned RNA sequences},
  journal = {Bioinformatics},
  abstract = {Motivation: In detection of non-coding RNAs, it is often necessary
  to identify the secondary structure motifs from a set of putative RNA
  sequences. Most of the existing algorithms aim to provide the best
  motif or few good motifs, but biologists often need to inspect all the
  possible motifs thoroughly.
  Results: Our method RNAmine employs a graph theoretic representation
  of RNA sequences, and detects all the possible motifs
  exhaustively using a graph mining algorithm. The motif detection problem
  boils down to finding frequently appearing patterns in a set of
  directed and labeled graphs. In the tasks of common secondary structure
  prediction and local motif detection from long sequences, our
  method performed favorably both in accuracy and in efficiency with
  the state-of-the-art methods such as CMFinder.},
  volume = {22},
  number = {20},
  pages = {2480-2487},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  month = oct,
  year = {2006},
  slug = {4144},
  author = {Hamada, M. and Tsuda, K. and Kudo, T. and Kin, T. and Asai, K.},
  month_numeric = {10}
}