Utilizing Selected Di- and Trinucleotides of SiRNA to Predict RNAi Activity

Overview

Journal Comput Math Methods Med

Publisher Hindawi

Specialty Medical Informatics

Date 2017 Mar 1

PMID 28243313

Citations 2

Authors

Ye Han

Yuanning Liu

Hao Zhang

Fei He

Chonghe Shu

Liyan Dong

Affiliations

Soon will be listed here.

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.

Citing Articles

siRNADiscovery: a graph neural network for siRNA efficacy prediction via deep RNA sequence analysis.

Long R, Guo Z, Han D, Liu B, Yuan X, Chen G Brief Bioinform. 2024; 25(6).

PMID: 39503523 PMC: 11539000. DOI: 10.1093/bib/bbae563.

SiRNA silencing efficacy prediction based on a deep architecture.

Han Y, He F, Chen Y, Liu Y, Yu H BMC Genomics. 2018; 19(Suppl 7):669.

PMID: 30255786 PMC: 6157246. DOI: 10.1186/s12864-018-5028-8.

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