@INPROCEEDINGS{SonRaeJagMue02,
    author = {Sonnenburg, S. and R\"atsch, G. and Jagota, A. and M\"uller, K.-R.},
    title = "New Methods for Splice-Site Recognition",
    booktitle = "In Proceedings of the International Conference on Artifical Neural Networks.",
    year = "2002",
    note = "Copyright by Springer",
    abstract = "Splice sites are locations in DNA which separate protein-codingregions (exons) from noncoding regions (introns). Accurate splicesite detectors thus form important components of computational genefinders. We pose splice site recognition as a classificationproblem with the classifier learnt from a labeled data setconsisting of only local information around the potential splicesite. Note that finding the correct position of splice siteswithout using global information is a rather hard task. We analyzethe genomes of the nematode Caenorhabditis elegans and of humansusing specially designed support vector kernels. One of the kernelsis adapted from our previous work on detecting translationinitiation sites in vertebrates and another uses an extension to thewell-known Fisher-kernel. We find excellent performance on both datasets.",
    dataset = "http://mlg.anu.edu.au/\textasciitilde raetsch/splice",
    pdf = "http://ida.first.fhg.de/\textasciitilde sonne/first/paper/pdf/SonRaeJagMue02.pdf.gz",
    ps = "http://ida.first.fhg.de/\textasciitilde sonne/first/paper/ps/SonRaeJagMue02.ps.gz"
}