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OJBTM
Online Journal of
Bioinformatics ©
Volume
12(1):1-8, 2011
Support
vector machine classification
and prediction of lyases
Lavanya Rishishwar1*,
Neha Mishra1, Bhasker
Pant1, Kumud Pant1,
Dr. K. R.
Pardasani2
Department
of Bioinformatics, Maulana Azad National
Institute of
Technology, Bhopal, India
Lavanya
Rishishwar L, Mishra
N, Pant
B, Pant K, Pardasani KR, Support Vector
Machine r
classification and prediction of lyases,
Online J
Bioinformatics, 12(1):1-9, 2011. A method for functionally
characterizing a novel enzyme by the application of suppo
rt vector
machines is
described. Optimal accuracy gained by this self consistency test is
91.42% with
Mathew's Correlation Coefficient (MCC) of 0.57. The method was further
validated by three different types of testing. The resulting accuracy
for the
LOO estimate was found to be 90.48% with MCC of 0.59 suggesting that
data was
not over fit.
Keywords: Lyases; Amino Acid Composition; Support Vector
Machine; RBF
kernel; Polynomial kernel; GRID.