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Erschienen in:
2002 | OriginalPaper | Buchkapitel
Predicting Signal Peptides with Support Vector Machines
verfasst von : Neelanjan Mukherjee, Sayan Mukherjee
Erschienen in: Pattern Recognition with Support Vector Machines
Verlag: Springer Berlin Heidelberg
Enthalten in: Professional Book Archive
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We examine using a Support Vector Machine to predict secretory signal peptides. We predict signal peptides for both prokaryotic and eukaryotic signal organisms. Signalling peptides versus non-signaling peptides as well as cleavage sites were predicted from a sequence of amino acids. Two types of kernels (each corresponding to different metrics) were used: hamming distance, a distance based upon the percent accepted mutation (PAM) score trained on the same signal peptide data.