Bioinformatics: the machine learning approach
Bioinformatics: the machine learning approach
Choosing Multiple Parameters for Support Vector Machines
Machine Learning
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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 nonsignaling 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.