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

ABSTRACT:

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, well-suited for interpretation of the learnt models. On the other h

SUBMITTER: Meinicke P 

PROVIDER: S-EPMC535353 | biostudies-literature | 2004 Oct

REPOSITORIES: biostudies-literature