Author/Authors :
Han, Ye Department of Computer Science and Technology - Jilin University - Changchun - Jilin, China , Liu, Yuanning Department of Computer Science and Technology - Jilin University - Changchun - Jilin, China , Zhang, Hao Department of Computer Science and Technology - Jilin University - Changchun - Jilin, China , He, Fei Department of Environment - Northeast Normal University - Changchun - Jilin, China , Shu, Chonghe Department of Computer Science and Technology - Jilin University - Changchun - Jilin, China , Dong, Liyan Department of Computer Science and Technology - Jilin University - Changchun - Jilin, China
Abstract :
Small interfering RNAs (siRNAs) induce posttranscriptional gene silencing in various organisms. siRNAs targeted to different
positions of the same gene show different effectiveness; hence, predicting siRNA activity is a crucial step. In this paper, we developed
and evaluated a powerful tool named “siRNApred” with a new mixed feature set to predict siRNA activity. To improve the prediction
accuracy, we proposed 2-3NTs as our new features. A Random Forest siRNA activity prediction model was constructed using the
feature set selected by our proposed Binary Search Feature Selection (BSFS) algorithm. Experimental data demonstrated that the
binding site of the Argonaute protein correlates with siRNA activity. “siRNApred” is effective for selecting active siRNAs, and
the prediction results demonstrate that our method can outperform other current siRNA activity prediction methods in terms of
prediction accuracy.
