@inproceedings{4157,
  title = {PALMA: Perfect Alignments using Large Margin Algorithms},
  journal = {Proceedings of the German Conference on Bioinformatics 2006 (GCB 2006)},
  booktitle = {GCB 2006},
  abstract = {Despite many years of research on how to properly align sequences in
  the presence of sequencing errors, alternative splicing and
  micro-exons, the correct alignment of mRNA sequences to genomic DNA is
  still a challenging task.  We present a novel approach based on large
  margin learning that combines kernel based splice site predictions
  with common sequence alignment techniques. By solving a convex
  optimization problem, our algorithm -- called PALMA -- tunes the
  parameters of the model such that the true alignment scores higher
  than all other alignments. In an experimental study on the alignments
  of mRNAs containing artificially generated micro-exons, we show that
  our algorithm drastically outperforms all other methods: It perfectly
  aligns all 4358 sequences on an hold-out set, while the best other
  method misaligns at least 90 of them. Moreover, our algorithm is very
  robust against noise in the query sequence: when deleting, inserting,
  or mutating up to 50% of the query sequence, it still aligns 95% of
  all sequences correctly, while other methods achieve less than 36%
  accuracy.  For datasets, additional results and a stand-alone
  alignment tool see
  http://www.fml.mpg.de/raetsch/projects/palma.},
  pages = {104-113},
  editors = {Huson, D. , O. Kohlbacher, A. Lupas, K. Nieselt, A. Zell},
  publisher = {Gesellschaft f{\"u}r Informatik},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {Bonn, Germany},
  month = sep,
  year = {2006},
  author = {R{\"a}tsch, G. and Hepp, B. and Schulze, U. and Ong, CS.},
  month_numeric = {9}
}