A novel model-based method for feature extraction from protein sequences for classification [Siniflandirma için protein dizilerinin özniteliklerinin çikarilmasinda model tabanli yeni bir yöntem]
2006 IEEE 14th Signal Processing and Communications Applications Conference
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Please cite this item using this persistent URLhttp://hdl.handle.net/11693/27176
Representation of amino-acid sequences constitutes the key point in classification of proteins into functional or structural classes. The representation should contain the biologically meaningful information hidden in the primary sequence of the protein. Conserved or similar subsequences are strong indicators of functional and structural similarity. In this study we present a feature mapping that takes into account the models of the subsequences of protein sequences. An expectation-maximization algorithm along with an HMM mixture model is used to cluster and learn the models of subsequences of a given set of proteins. © 2006 IEEE.
- Conference Paper 2294