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dc.contributor.authorChalk, A.
dc.contributor.authorL.L. Sonnhammer, Erik
dc.date.accessioned2017-05-03T15:23:17Z
dc.date.available2017-05-03T15:23:17Z
dc.date.issued2002
dc.date.modified2009-08-26T07:06:32Z
dc.identifier.issn13674803
dc.identifier.doi10.1093/bioinformatics/18.12.1567
dc.identifier.urihttp://hdl.handle.net/10072/20944
dc.description.abstractMotivation: The expression of a gene can be selectively inhibited by antisense oligonucleotides (AOs) targeting the mRNA. However, if the target site in the mRNA is picked randomly, typically 20% or less of the AOs are effective inhibitors in vivo. The sequence properties that make an AO effective are not well understood, thus many AOs need to be tested to find good inhibitors, which is time consuming and costly. So far computational models have been based exclusively on RNA structure prediction or motif searches while ignoring information from other aspects of AO design into the model. Results: We present a computational model for AO prediction based on a neural network approach using a broad range of input parameters. Collecting sequence and efficacy data from AO scanning experiments in the literature generated a database of 490 AO molecules. Using a set of derived parameters based on AO sequence properties we trained a neural network model. The best model, an ensemble of 10 networks, gave an overall correlation coefficient of 0.30 (p=10-8). This model can predict effective AOs (>50% inhibition of gene expression) with a success rate of 92%. Using these thresholds the model predicts on average 12 effective AOs per 1000 base pairs, making it a stringent yet practical method for AO prediction. Availability: A prediction server is available at http://www.cgb.ki.se/AOpredict
dc.description.peerreviewedYes
dc.description.publicationstatusYes
dc.format.extent91871 bytes
dc.format.mimetypeapplication/pdf
dc.languageEnglish
dc.language.isoeng
dc.publisherOxford University Press
dc.publisher.placeOxford, UK
dc.publisher.urihttp://bioinformatics.oxfordjournals.org/
dc.relation.ispartofpagefrom1567
dc.relation.ispartofpageto1575
dc.relation.ispartofissue12
dc.relation.ispartofjournalBioinformatics
dc.relation.ispartofvolume18
dc.subject.fieldofresearchMathematical Sciences
dc.subject.fieldofresearchBiological Sciences
dc.subject.fieldofresearchInformation and Computing Sciences
dc.subject.fieldofresearchcode01
dc.subject.fieldofresearchcode06
dc.subject.fieldofresearchcode08
dc.titleComputational Antisense Oligo Prediction with a Neural Network Model.
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
gro.rights.copyrightThis is a pre-copy-editing, author-produced PDF of an article accepted for publication in Bioinformatics following peer review. The definitive publisher-authenticated version Bioinformatics 2002 8(12):1567-75 is available online at: http://dx.doi.org/10.1093/bioinformatics/18.12.1567
gro.date.issued2002
gro.hasfulltextFull Text
gro.griffith.authorChalk, Alistair M.


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