TY - JOUR
T1 - Prediction of siRNA knockdown efficiency using artificial neural network models
AU - Ge, Guangtao
AU - Wong, G. William
AU - Luo, Biao
N1 - Funding Information:
G.W.W. is supported by the NIH NRSA postdoctorate fellowship (5F32DK 067835-02).
Copyright:
Copyright 2008 Elsevier B.V., All rights reserved.
PY - 2005/10/21
Y1 - 2005/10/21
N2 - Selective knockdown of gene expression by short interference RNAs (siRNAs) has allowed rapid validation of gene functions and made possible a high throughput, genome scale approach to interrogate gene function. However, randomly designed siRNAs display different knockdown efficiencies of target genes. Hence, various prediction algorithms based on siRNA functionality have recently been constructed to increase the likelihood of selecting effective siRNAs, thereby reducing the experimental cost. Toward this end, we have trained three Back-propagation and Bayesian neural network models, previously not used in this context, to predict the knockdown efficiencies of 180 experimentally verified siRNAs on their corresponding target genes. Using our input coding based primarily on RNA structure thermodynamic parameters and cross-validation method, we showed that our neural network models outperformed most other methods and are comparable to the best predicting algorithm thus far published. Furthermore, our neural network models correctly classified 74% of all siRNAs into different efficiency categories; with a correlation coefficient of 0.43 and receiver operating characteristic curve score of 0.78, thus highlighting the potential utility of this method to complement other existing siRNA classification and prediction schemes.
AB - Selective knockdown of gene expression by short interference RNAs (siRNAs) has allowed rapid validation of gene functions and made possible a high throughput, genome scale approach to interrogate gene function. However, randomly designed siRNAs display different knockdown efficiencies of target genes. Hence, various prediction algorithms based on siRNA functionality have recently been constructed to increase the likelihood of selecting effective siRNAs, thereby reducing the experimental cost. Toward this end, we have trained three Back-propagation and Bayesian neural network models, previously not used in this context, to predict the knockdown efficiencies of 180 experimentally verified siRNAs on their corresponding target genes. Using our input coding based primarily on RNA structure thermodynamic parameters and cross-validation method, we showed that our neural network models outperformed most other methods and are comparable to the best predicting algorithm thus far published. Furthermore, our neural network models correctly classified 74% of all siRNAs into different efficiency categories; with a correlation coefficient of 0.43 and receiver operating characteristic curve score of 0.78, thus highlighting the potential utility of this method to complement other existing siRNA classification and prediction schemes.
KW - Artificial neural network
KW - Knockdown efficiency
KW - siRNA
UR - http://www.scopus.com/inward/record.url?scp=24644433426&partnerID=8YFLogxK
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U2 - 10.1016/j.bbrc.2005.08.147
DO - 10.1016/j.bbrc.2005.08.147
M3 - Article
C2 - 16153609
AN - SCOPUS:24644433426
SN - 0006-291X
VL - 336
SP - 723
EP - 728
JO - Biochemical and Biophysical Research Communications
JF - Biochemical and Biophysical Research Communications
IS - 2
ER -