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. 2018 Sep 24;19(Suppl 7):669.
doi: 10.1186/s12864-018-5028-8.

SiRNA silencing efficacy prediction based on a deep architecture

Ye Han  1 , Fei He  2   3 , Yongbing Chen  2   3 , Yuanning Liu  4   5 , Helong Yu  6
Affiliations

SiRNA silencing efficacy prediction based on a deep architecture

Ye Han et al. BMC Genomics. .

Abstract

Background: Small interfering RNA (siRNA) can be used to post-transcriptional gene regulation by knocking down targeted genes. In functional genomics, biomedical research and cancer therapeutics, siRNA design is a critical research topic. Various computational algorithms have been developed to select the most effective siRNA, whereas the efficacy prediction accuracy is not so satisfactory. Many existing computational methods are based on feature engineering, which may lead to biased and incomplete features. Deep learning utilizes non-linear mapping operations to detect potential feature pattern and has been considered perform better than existing machine learning method.

Results: In this paper, to further improve the prediction accuracy and facilitate gene functional studies, we developed a new powerful siRNA efficacy predictor based on a deep architecture. First, we extracted hidden feature patterns from two modalities, including sequence context features and thermodynamic property. Then, we constructed a deep architecture to implement the prediction. On the available largest siRNA database, the performance of our proposed method was measured with 0.725 PCC and 0.903 AUC value. The comparative experiment showed that our proposed architecture outperformed several siRNA prediction methods.

Conclusions: The results demonstrate that our deep architecture is stable and efficient to predict siRNA silencing efficacy. The method could help select candidate siRNA for targeted mRNA, and further promote the development of RNA interference.

Keywords: Deep learning; RNAi; siRNA.

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The authors consent to publish this paper to BMC Genomics.

Competing interests

The authors declare that they have no competing interests.

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Figures

Fig. 1
Fig. 1
The deep architecture of our siRNA eddicacy prediction model
Fig. 2
Fig. 2
The influence of the length of flanking nucleotides on prediction result
Fig. 3
Fig. 3
The influence of the size of convolution kernel on prediction result
Fig. 4
Fig. 4
The influence of activation function on prediction result
Fig. 5
Fig. 5
The influence of learning rate on prediction result
Fig. 6
Fig. 6
The comparison among five algorithms
See this image and copyright information in PMC

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