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Publications [#323275] of Sayan Mukherjee

Papers Published

  1. Mukherjee, N; Mukherjee, S, Predicting signal peptides with support vector machines, Lecture notes in computer science, vol. 2388 (January, 2002), pp. 1-7, ISBN 354044016X [doi]
    (last updated on 2017/04/01)

    Abstract:
    © Springer-Verlag Berlin Heidelberg 2002.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.

 

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