Predicting the efficacy of short oligonucleotides in antisense and RNAi experiments with boosted genetic programming

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@Article{Saetrom:2004:BI,
  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

                 \cite{Matveeva:2007:NAR}

                 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",
}

Genetic Programming entries for Pal Saetrom

Citations