PredCRG (version 1.0.2)

model1: Trained model with the Q1 dataset.

Description

The model1 is the trained model with the Q1 dataset using the developed approach.

Usage

data("model1")

Details

Here, 1558 sequences of pos_Q1 and neg_Q1 datasets were used for training. For prediction, support vector machine with Laplace kernel has been trained in which compositionsl, transitional and physico-chemical features are utilized.

See Also

PredCRG, PredCRG_Enc, PredCRG_training

Examples

# NOT RUN {
library(kernlab)
data(test)
nam <- names(test)

#encoding of test set using compositional, transitional and physico-chemical features
enc <- PredCRG_Enc(test)

#predicting test set using model1 as CRG or non-CRG
pred <- predict(model1, newdata=enc[1:10,], type="response") 

#predicting probabilities of the test sequences using model1
pred1 <- predict(model1, newdata=enc[1:10,], type="probabilities") 

#combining predicted labels and probabilities
result <- data.frame(seq_name=nam[1:10], predicted_label=as.character(pred)
,predicted_probability=pred1[,"CRG"])

print(result)
# }