Amino acids were represented as binary string of length 20 where 19 "0"
and a unique position set to "1" for each amino acid.For example an amino
acid(A) can be represented as follows
Amino Acid Composition
Amino acid composition is the fraction of each amino acid in a protein.
SVM
The SVM was implemented using freely downloadable software package
SVM_light written by Joachims (Joachims 1999). The software enables the
user to define a number of parameters as well as to select from a choice
of inbuilt kernal functions, including a radial basis function (RBF) and a
polynomial kernal.
ANN
ANN was trained by using the Stuttgart Neural Network Simulator, SNNS
version 4.2(2).The advantage of this package is that it allows
incorporation of resulting networks in ANSI C functions for use in stand
alone code. The critical step in ANN is the optimization of the hidden
nodes and other learning parameters. Here we have used the feed forward
back propagation type of ANN with single hidden layer having 20 nodes,
300(20X15) input units and 1 output unit.
Evaluation Modules
The performance modules constructed in this study were evaluated using a
5-fold cross-validation technique. In the 5-fold cross-validation, the
relevant dataset was partoned randomly into five equally sized sets. The
training and testing was carried out five times, each time using one
distinct set for testing and the remaining four sets for training.The
performance of the methods was computed using the following formulas
Sensitivity = TP/TP+FNX100
Specificity = TN/TN+FPX100
Accuracy = TP+TN/TP+FP+TN+FN
Where TP and TN are correctly predicted antibacterial peptides and non
antibacterial peptides respectively. FP and FN are wrongly predicted
antibacterial peptides and non antibacterial peptides
respectively.