Predicting siRNA efficacy based on multiple selective siRNA representations and their combination at score level.
Small interfering RNAs (siRNAs) may induce to targeted gene knockdown, and the gene silencing effectiveness relies on the efficacy of the siRNA. Therefore, the task of this paper is to construct an effective siRNA prediction method. In our work, we try to describe siRNA from both quantitative and qualitative aspects. For quantitative analyses, we form four groups of effective features, including nucleotide frequencies, thermodynamic stability profile, thermodynamic of siRNA-mRNA interaction, and mRNA related features, as a new mixed representation, in which thermodynamic of siRNA-mRNA interaction is introduced to siRNA efficacy prediction for the first time to our best knowledge. And then an F-score based feature selection is employed to investigate the contribution of each feature and remove the weak relevant features. Meanwhile, we encode the siRNA sequence and existed empirical design rules as a qualitative siRNA representation. These two kinds of siRNA representations are combined to predict siRNA efficacy by supported Vector Regression (SVR) at score level. The experimental results indicate that our method may select the features with powerful discriminative ability and make the two kinds of siRNA representations work at full capacity. The prediction results also demonstrate that our method can outperform other popular siRNA efficacy prediction algorithms.
Northeast Normal University, School of Computer Science and Information Technology, Changchun, 130117, China.|Northeast Normal University, School of Environment, Changchun, 130117, China.|Northeast Normal University, Institute of Computational Biology, Changchun, 130117, China.
Fei
F
He
Jilin University, College of Computer Science and Technology, Changchun, 130012, China.|Jilin University, Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Changchun, 130012, China.
Ye
Y
Han
Northeast Normal University, School of Computer Science and Information Technology, Changchun, 130117, China.|Northeast Normal University, Institute of Computational Biology, Changchun, 130117, China.
Jianting
J
Gong
Northeast Normal University, School of Computer Science and Information Technology, Changchun, 130117, China.|Northeast Normal University, Institute of Computational Biology, Changchun, 130117, China.
Jiazhi
J
Song
Northeast Normal University, School of Computer Science and Information Technology, Changchun, 130117, China.|Northeast Normal University, Institute of Computational Biology, Changchun, 130117, China.
Han
H
Wang
Northeast Normal University, School of Computer Science and Information Technology, Changchun, 130117, China.|Northeast Normal University, Institute of Computational Biology, Changchun, 130117, China.
Yanwen
Y
Li
D000465:Algorithms; D001482:Base Composition; D019295:Computational Biology / Q000379:methods; D005786:Gene Expression Regulation; D034622:RNA Interference; D012333:RNA, Messenger / Q000737:chemistry* / Q000235:genetics*; D034741:RNA, Small Interfering / Q000737:chemistry* / Q000235:genetics*; D012372:ROC Curve; D015203:Reproducibility of Results; D013816:Thermodynamics
D012333:RNA, Messenger; D034741:RNA, Small Interfering