model3: Trained model with the Q3 dataset.

In PredCRG: Computational Prediction of Proteins Encoded by Circadian Genes

Description

The model3 is the trained model with the Q3 dataset using the developed approach.

Usage

data("model3")

Details

Here, 1593 sequences of pos_Q3 and neg_Q3 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

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 model3 as CRG or non-CRG
pred <- predict(model3, newdata=enc[1:10,], type="response") 

#predicting probabilities of the test sequences using model3
pred1 <- predict(model3, 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)

PredCRG documentation built on Dec. 15, 2020, 5:35 p.m.