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ALGORITHMData Sets
Antibacterial peptides data used here were taken from the APD database. APD database contains 500 antibacterial peptides. Antibacterial peptides having less than 15 amino acid residues and duplicate peptides were removed. Non-antibacterial peptides have been taken randomly. Final dataset contains 391 antibacterial peptides and same number of non antibacterial peptides(randomly taken). INPUT Binary patterns and amino acid composition were used for the input. Binary Patterns 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 A = 10000000000000000000 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
Evaluation ModulesANN 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. 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. |