@InProceedings{SonRaeJagMue02,
	author =       {S\"oren Sonnenburg and Gunnar R\"atsch and Arun Jagota and Klaus-Robert M\"uller},
	title =        {New Methods for Splice-Site Recognition},
	note =         {Copyright by Springer},
	booktitle =    {In Proceedings of the International Conference on Artifical Neural Networks.},
	year =         {2002},
	pages     = {329--336},
	ps =           {http://sonnenburgs.de/soeren/publications/SonRaeJagMue02.ps.gz},
	pdf =          {http://sonnenburgs.de/soeren/publications/SonRaeJagMue02.pdf.gz},
	dataset = {http://www.fml.tuebingen.mpg.de/raetsch/projects/AnuSplice/},
	abstract = 	 {
			Splice sites are locations in DNA which separate protein-coding
			regions (exons) from noncoding regions (introns).  Accurate splice
			site detectors thus form important components of computational gene
			finders.  We pose splice site recognition as a classification
			problem with the classifier learnt from a labeled data set
			consisting of only local information around the potential splice
			site.  Note that finding the correct position of splice sites
			without using global information is a rather hard task. We analyze
			the genomes of the nematode Caenorhabditis elegans and of humans
			using specially designed support vector kernels.  One of the kernels
			is adapted from our previous work on detecting translation
			initiation sites in vertebrates and another uses an extension to the
			well-known Fisher-kernel.  We find excellent performance on both data
			sets.
	}

}