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.
CITATION STYLE
Mukherjee, N., & Mukherjee, S. (2002). Predicting signal peptides with support vector machines. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2388, pp. 1–7). Springer Verlag. https://doi.org/10.1007/3-540-45665-1_1
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