- author = "Pal Saetrom",
- title = "Predicting the efficacy of short oligonucleotides in antisense and {RNAi} experiments with boosted genetic programming",
- journal = "Bioinformatics",
- year = "2004",
- volume = "20",
- number = "17",
- pages = "3055--3063",
- month = nov # " 22",
- keywords = "genetic algorithms, genetic programming",
- language = "en",
- oai = "oai:CiteSeerX.psu:10.1.1.612.5977",
-
URL = "
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.612.5977",
-
URL = "
http://bioinformatics.oxfordjournals.org/content/20/17/3055.full.pdf",
-
DOI = "
doi:10.1093/bioinformatics/bth364",
- size = "9 pages",
-
abstract = "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.",
-
notes = "Cited by AsiDesigner: exon-based siRNA design server
considering alternative splicing Young-Kyu Park,
Seong-Min Park, Young-Chul Choi, Doheon Lee, Misun Won,
and Young Joo Kim Nucleic Acids Res. 2008 July 1;
36(Web Server issue): W97-W103. Published online 2008
July 1. doi:10.1093/nar/gkn280. PMCID:
PMC2447810
Thermodynamic instability of siRNA duplex is a prerequisite for dependable prediction of siRNA activities Masatoshi Ichihara, Yoshiki Murakumo, Akio Masuda, Toru Matsuura, Naoya Asai, Mayumi Jijiwa, Maki Ishida, Jun Shinmi, Hiroshi Yatsuya, Shanlou Qiao, Masahide Takahashi, and Kinji Ohno Nucleic Acids Res. 2007 September; 35(18): e123. Published online 2007 September. doi:10.1093/nar/gkm699. PMCID: PMC2094068
More complete gene silencing by fewer siRNAs: transparent optimized design and biophysical signature Istvan Ladunga Nucleic Acids Res. 2007 January; 35(2): 433-440. Published online 2006 December 14. doi:10.1093/nar/gkl1065. PMCID: PMC1802606
An accurate and interpretable model for siRNA efficacy prediction Jean-Philippe Vert, Nicolas Foveau, Christian Lajaunie, and Yves Vandenbrouck BMC Bioinformatics. 2006; 7: 520. Published online 2006 November 30. doi:10.1186/1471-2105-7-520. PMCID: PMC1698581
Selection of antisense oligonucleotides based on multiple predicted target mRNA structures Xiaochen Bo, Shaoke Lou, Daochun Sun, Wenjie Shu, Jing Yang, and Shengqi Wang BMC Bioinformatics. 2006; 7: 122. Published online 2006 March 9. doi:10.1186/1471-2105-7-122. PMCID: PMC1421440
Computational models with thermodynamic and composition features improve siRNA design Svetlana A Shabalina, Alexey N Spiridonov, and Aleksey Y Ogurtsov BMC Bioinformatics. 2006; 7: 65. Published online 2006 February 12. doi:10.1186/1471-2105-7-65. PMCID: PMC1431570",
