Bioinformatics Advance Access originally published online on June 16, 2004
Bioinformatics 2004 20(17):3055-3063; doi:10.1093/bioinformatics/bth364
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Bioinformatics vol. 20 issue 17 © Oxford University Press 2004; all rights reserved.
Predicting the efficacy of short oligonucleotides in antisense and RNAi experiments with boosted genetic programming
Interagon AS, Medisinsk teknisk senter, N-7489 Trondheim, Norway
Received on March 26, 2004; accepted on June 10, 2004
Advance Access Publication June 17, 2004
Motivation: Both small interfering RNAs (siRNAs) and antisense oligonucleotides can selectively block gene expression. Although the two methods rely on different cellular mechanisms, these methods share the common property that not all oligonucleotides (oligos) are equally effective. That is, if mRNA target sites are picked at random, many of the antisense or siRNA oligos will not be effective. Algorithms that can reliably predict the efficacy of candidate oligos can greatly reduce the cost of knockdown experiments, but previous attempts to predict the efficacy of antisense oligos have had limited success. Machine learning has not previously been used to predict siRNA efficacy.
Results: We develop a genetic programming based prediction system that shows promising results on both antisense and siRNA efficacy prediction. We train and evaluate our system on a previously published database of antisense efficacies and our own database of siRNA efficacies collected from the literature. The best models gave an overall correlation between predicted and observed efficacy of 0.46 on both antisense and siRNA data. As a comparison, the best correlations of support vector machine classifiers trained on the same data were 0.40 and 0.30, respectively.
Availability: The prediction system uses proprietary hardware and is available for both commercial and strategic academic collaborations. The siRNA database is available upon request.
Contact: paal.saetrom{at}interagon.com
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