Prediction of siRNA knockdown efficiency using artificial neural network models
- Department of Computer Science, Tufts University, 161 College Avenue, Medford, MA 02155 (United States) and Whitehead Institute for Biomedical Research, 9 Cambridge Center, Cambridge, MA 02142 (United States)
- Whitehead Institute for Biomedical Research, 9 Cambridge Center, Cambridge, MA 02142 (United States)
- RNAi Consortium, Broad Institute, Massachusetts Institute of Technology, 320 Bent Street, Cambridge, MA 02142 (United States)
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.
- OSTI ID:
- 20713398
- Journal Information:
- Biochemical and Biophysical Research Communications, Vol. 336, Issue 2; Other Information: DOI: 10.1016/j.bbrc.2005.08.147; PII: S0006-291X(05)01719-5; Copyright (c) 2005 Elsevier Science B.V., Amsterdam, The Netherlands, All rights reserved; Country of input: International Atomic Energy Agency (IAEA); ISSN 0006-291X
- Country of Publication:
- United States
- Language:
- English
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