» Home
» Submit
» Help
» Algorithm
» Contact
» Team
» Suplementary
» Standalone
|
|
| Algorithm |
|
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.
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+FN))*100
Specificity = (TN / (TN+FP))*100
Accuracy = (TP+TN / (TP+FP+TN+FN))*100
Where TP and TN are correctly predicte GST proteins and
non-GST proteins respectively. FP and FN are wrongly predicted
GST proteins and non GST proteins respectively.
Bioinformatics Center, Institute Of Microbial Technology,Chandigarh,India
|
|
