Artificial neural network prediction of antisense oligodeoxynucleotide activity

An mRNA transcript contains many potential antisense oligodeoxynucleotide target sites. Identifi cation 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 ac...

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Principais autores: Giddings, Michael C., Shah, Atul A., Freier, Sue, Atkins, John F., Gesteland, Raymond F., Matveeva, Olga V.
Formato: Artigo
Idioma:en
Publicado em: Oxford University Press 2002
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Acesso em linha:https://ncbi.nlm.nih.gov/pmc/articles/PMC140555/
https://ncbi.nlm.nih.gov/pubmed/12364609
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spelling pubmed-1405552003-01-30 Artificial neural network prediction of antisense oligodeoxynucleotide activity Giddings, Michael C. Shah, Atul A. Freier, Sue Atkins, John F. Gesteland, Raymond F. Matveeva, Olga V. Nucleic Acids Res Articles An mRNA transcript contains many potential antisense oligodeoxynucleotide target sites. Identifi cation 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. Oxford University Press 2002-10-01 /pmc/articles/PMC140555/ /pubmed/12364609 Text en Copyright © 2002 Oxford University Press
institution US National Library of Medicine
collection PubMed Central
language en
format Article
topic Articles
spellingShingle Articles
Giddings, Michael C.
Shah, Atul A.
Freier, Sue
Atkins, John F.
Gesteland, Raymond F.
Matveeva, Olga V.
Artificial neural network prediction of antisense oligodeoxynucleotide activity
description An mRNA transcript contains many potential antisense oligodeoxynucleotide target sites. Identifi cation 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.
author Giddings, Michael C.
Shah, Atul A.
Freier, Sue
Atkins, John F.
Gesteland, Raymond F.
Matveeva, Olga V.
author_facet Giddings, Michael C.
Shah, Atul A.
Freier, Sue
Atkins, John F.
Gesteland, Raymond F.
Matveeva, Olga V.
author_sort Giddings, Michael C.
title Artificial neural network prediction of antisense oligodeoxynucleotide activity
title_short Artificial neural network prediction of antisense oligodeoxynucleotide activity
title_full Artificial neural network prediction of antisense oligodeoxynucleotide activity
title_fullStr Artificial neural network prediction of antisense oligodeoxynucleotide activity
title_full_unstemmed Artificial neural network prediction of antisense oligodeoxynucleotide activity
title_sort artificial neural network prediction of antisense oligodeoxynucleotide activity
publisher Oxford University Press
publisher_facet Oxford University Press
publishDate 2002
url https://ncbi.nlm.nih.gov/pmc/articles/PMC140555/
https://ncbi.nlm.nih.gov/pubmed/12364609
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