Biology – Quantitative Biology – Genomics
Scientific paper
2003-10-16
Biology
Quantitative Biology
Genomics
12 pages, 1 figure
Scientific paper
10.1109/IJCNN.2004.1380902
We propose a new kernel for biological sequences which borrows ideas and techniques from information theory and data compression. This kernel can be used in combination with any kernel method, in particular Support Vector Machines for protein classification. By incorporating prior biological assumptions on the properties of amino-acid sequences and using a Bayesian averaging framework, we compute the value of this kernel in linear time and space, benefiting from previous achievements proposed in the field of universal coding. Encouraging classification results are reported on a standard protein homology detection experiment.
Cuturi Marco
Vert Jean-Philippe
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