PredCRG (version 1.0.2)

PredCRG_training: Training of the PredCRG model using the user supplied sequence dataset.

Description

User can build their own PredCRG model by using their own training dataset. User has to supply the protein sequence dataset of both positive and negative classes having standard amino acid residues only.

Usage

PredCRG_training(pos_seq, neg_seq, kern)

Arguments

pos_seq

circadian protein sequence dataset (also called positive dataset), must be an object of class AAStringSet.

neg_seq

non-circadian protein sequence dataset (also called negative dataset), must be an object of class AAStringSet.

kern

Type of kernel to be used. It may be laplace, linear, polynomial or RBF.

Value

Support Vector Machine object of class ksvm

Details

The sequences must of AAStringSet type can be obtained by reading the FASTA file of the sequences using function readAAStringSet available in Biostrings package.

See Also

PredCRG, PredCRG_Enc, model1, model2,model3,model4

Examples

# NOT RUN {
library(kernlab)
pos_Q1 <- PredCRG_data$pos_Q1
neg_Q1 <- PredCRG_data$neg_Q1

#training of the model using laplace kernel.
user_model <- PredCRG_training(pos_seq=pos_Q1[1:100], neg_seq=neg_Q1[1:100], kern="laplace")

data(test)
tst_enc <- PredCRG_Enc(test[1:10])#encoding of the test set
predict(user_model, tst_enc, type="response") #predicting the label of the test instances
predict(user_model, tst_enc, type="probabilities")#predicting the probability of the test instances


library(e1071)
#training of the model using RBF kernel.
user_model <- PredCRG_training(pos_seq=pos_Q1[1:100], neg_seq=neg_Q1[1:100], kern="RBF")
predict(user_model, tst_enc, probability=TRUE) #Predicting probability
predict(user_model, tst_enc) #Predicting labels
# }