Performance evaluation of neural network, support vector machine and random forest for prediction of donor splice sites in rice

Abstract

Prediction of splice sites plays an important role in predicting the gene structure. Rice being one of the major cereal crops, continuous improvement is possible with the prediction of unknown genes associated with complex traits. Machine learning techniques i.e., Artificial Neural Network (ANN) and Support Vector Machine (SVM) have been successfully used for the prediction of splice sites but comparison of their performance has not been made yet to our limited knowledge. Further, Random Forest (RF), another machine learning method, has been successfully used and reported to outperform ANN and SVM in areas other than splice site prediction. In this study we have developed an approach to encode the splice site sequence data of rice into numeric form that are subsequently used as input in ANN, SVM and RF for prediction of donor splice sites. The performances were then evaluated and compared using receiving operating characteristics (ROC) curve and estimate of area under ROC curve (AUC), averaged over 5-fold cross validation. The result reveals that AUC of RF is higher than ANN and SVM which implies that it can be preferred over SVM and ANN in the prediction splice sites.