ArticleNucleic acids research2002
Artificial neural network prediction of antisense oligodeoxynucleotide activity.
Article in Nucleic acids research, 2002. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
11 citing papers in PubMed.
- siRNAs target sites selection of ezrin and the influence of RNA interference on ezrin expression and biological characters of osteosarcoma cells.Molecular and cellular biochemistry · 2012Article
- Prediction of antisense oligonucleotides using structural and thermodynamic motifs.Bioinformation · 2012Article
- Ezrin mRNA target site selection for DNAzymes using secondary structure and hybridization thermodynamics.Tumour biology : the journal of the International Society for Oncodevelopmental Biology and Medicine · 2011Article
- Identification of sequence motifs significantly associated with antisense activity.BMC bioinformatics · 2007Article
- Selection of antisense oligonucleotides based on multiple predicted target mRNA structures.BMC bioinformatics · 2006Article
- AOBase: a database for antisense oligonucleotides selection and design.Nucleic acids research · 2006Article
- Design of a genome-wide siRNA library using an artificial neural network.Nature biotechnology · 2005Article
- Selection of optimal antisense accessible sites of survivin and its application in treatment of gastric cancer.World journal of gastroenterology · 2005Article
- Profiled support vector machines for antisense oligonucleotide efficacy prediction.BMC bioinformatics · 2004Article
- mRNA fusion constructs serve in a general cell-based assay to profile oligonucleotide activity.Nucleic acids research · 2003Article
- A software tool-box for analysis of regulatory RNA elements.Nucleic acids research · 2003Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
An mRNA transcript contains many potential antisense oligodeoxynucleotide target sites. Identification of the most efficacious targets remains an important and challenging problem. Building on separate work that revealed a strong correlation between the inclusion of short sequence motifs and the activity level of an oligo, we have developed a predictive artificial neural network system for mapping tetranucleotide motif content to antisense oligo activity. Trained for high-specificity prediction, the system has been cross-validated against a database of 348 oligos from the literature and a larger proprietary database of 908 oligos. In cross- validation tests the system identified effective oligos (i.e. oligos capable of reducing target mRNA expression to <25% that of the control) with 53% accuracy, in contrast to the <10% success rates commonly reported for trial-and-error oligo selection, suggesting a possible 5-fold reduction in the in vivo screening required to find an active oligo. We have implemented a web interface to a trained neural network. Given an RNA transcript as input, the system identifies the most likely oligo targets and provides estimates of the probabilities that oligos targeted against these sites will be effective.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.