Oligo kernels for datamining on biological sequences: a case study on prokaryotic translation initiation sites

Background: Kernel-based learning algorithms are among the most advanced machine learning methods and have been successfully applied to a variety of sequence classification tasks within the field of bioinformatics. Conventional kernels utilized so far do not provide an easy interpretation of the learnt representations in terms of positional and compositional variability of the underlying biological signals. Results: We propose a kernel-based approach to datamining on biological sequences. With our method it is possible to model and analyze positional variability of oligomers of any length in a natural way. On one hand this is achieved by mapping the sequences to an intuitive but high-dimensional feature space, wellsuited for interpretation of the learnt models. On the other hand, by means of the kernel trick we can provide a general learning algorithm for that high-dimensional representation because all required statistics can be computed without performing an explicit feature space m...

• Type: Journal
• Year: 2004
• Where: BMCBI
• Authors: Peter Meinicke, Maike Tech, Burkhard Morgenstern, Rainer Merkl
• Keywords: BMCBI 2004, Feature Space, Learning Algorithm, Sequences