title: Utilizing Selected Di- and Trinucleotides of siRNA to Predict RNAi Activity.
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.
authors.affiliation: Department of Computer Science and Technology, Jilin University, Changchun, Jilin, China; Key Laboratory of Symbolic Computation and Knowledge Engineering, Ministry of Education, Jilin University, Changchun, China.
authors.forename: Ye
authors.initials: Y
authors.lastname: Han
authors.affiliation: Department of Computer Science and Technology, Jilin University, Changchun, Jilin, China; Key Laboratory of Symbolic Computation and Knowledge Engineering, Ministry of Education, Jilin University, Changchun, China.
authors.forename: Yuanning
authors.initials: Y
authors.lastname: Liu
authors.affiliation: Department of Computer Science and Technology, Jilin University, Changchun, Jilin, China; Key Laboratory of Symbolic Computation and Knowledge Engineering, Ministry of Education, Jilin University, Changchun, China.
authors.forename: Hao
authors.initials: H
authors.lastname: Zhang
authors.affiliation: Department of Computer Science and Information Technology, Northeast Normal University, Changchun, Jilin, China; Department of Environment, Northeast Normal University, Changchun, Jilin, China; Institute of Computational Biology, Northeast Normal University, Changchun, China.
authors.forename: Fei
authors.initials: F
authors.lastname: He
authors.affiliation: Department of Computer Science and Technology, Jilin University, Changchun, Jilin, China; Key Laboratory of Symbolic Computation and Knowledge Engineering, Ministry of Education, Jilin University, Changchun, China.
authors.forename: Chonghe
authors.initials: C
authors.lastname: Shu
authors.affiliation: Department of Computer Science and Technology, Jilin University, Changchun, Jilin, China; Key Laboratory of Symbolic Computation and Knowledge Engineering, Ministry of Education, Jilin University, Changchun, China.
authors.forename: Liyan
authors.identifier: 0000-0001-7491-5893
authors.initials: L
authors.lastname: Dong
mesh_terms: D000465:Algorithms; D000818:Animals; D019295:Computational Biology; D006801:Humans; D051379:Mice; D008957:Models, Genetic; D009711:Nucleotides / Q000235:genetics; D034622:RNA Interference; D034741:RNA, Small Interfering / Q000378:metabolism*; D012372:ROC Curve; D051381:Rats; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D017423:Sequence Analysis, RNA; D013816:Thermodynamics
chemical_list: D009711:Nucleotides; D034741:RNA, Small Interfering