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ARH: predicting splice variants from genome-wide data with modified entropy
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Zeitschriftentitel: | Bioinformatics |
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Personen und Körperschaften: | , |
In: | Bioinformatics, 26, 2010, 1, S. 84-90 |
Format: | E-Article |
Sprache: | Englisch |
veröffentlicht: |
Oxford University Press (OUP)
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Schlagwörter: |
author_facet |
Rasche, Axel Herwig, Ralf Rasche, Axel Herwig, Ralf |
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author |
Rasche, Axel Herwig, Ralf |
spellingShingle |
Rasche, Axel Herwig, Ralf Bioinformatics ARH: predicting splice variants from genome-wide data with modified entropy Computational Mathematics Computational Theory and Mathematics Computer Science Applications Molecular Biology Biochemistry Statistics and Probability |
author_sort |
rasche, axel |
spelling |
Rasche, Axel Herwig, Ralf 1367-4811 1367-4803 Oxford University Press (OUP) Computational Mathematics Computational Theory and Mathematics Computer Science Applications Molecular Biology Biochemistry Statistics and Probability http://dx.doi.org/10.1093/bioinformatics/btp626 <jats:title>Abstract</jats:title> <jats:p>Motivation: Exon arrays allow the quantitative study of alternative splicing (AS) on a genome-wide scale. A variety of splicing prediction methods has been proposed for Affymetrix exon arrays mainly focusing on geometric correlation measures or analysis of variance. In this article, we introduce an information theoretic concept that is based on modification of the well-known entropy function.</jats:p> <jats:p>Results: We have developed an AS robust prediction method based on entropy (ARH). We can show that this measure copes with bias inherent in the analysis of AS such as the dependency of prediction performance on the number of exons or variable exon expression. In order to judge the performance of ARH, we have compared it with eight existing splicing prediction methods using experimental benchmark data and demonstrate that ARH is a well-performing new method for the prediction of splice variants.</jats:p> <jats:p>Availability and Implementation: ARH is implemented in R and provided in the Supplementary Material.</jats:p> <jats:p>Contact: rasche@molgen.mpg.de</jats:p> <jats:p>Supplementary information: Supplementary data are available at Bioinformatics online.</jats:p> ARH: predicting splice variants from genome-wide data with modified entropy Bioinformatics |
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10.1093/bioinformatics/btp626 |
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title |
ARH: predicting splice variants from genome-wide data with modified entropy |
title_unstemmed |
ARH: predicting splice variants from genome-wide data with modified entropy |
title_full |
ARH: predicting splice variants from genome-wide data with modified entropy |
title_fullStr |
ARH: predicting splice variants from genome-wide data with modified entropy |
title_full_unstemmed |
ARH: predicting splice variants from genome-wide data with modified entropy |
title_short |
ARH: predicting splice variants from genome-wide data with modified entropy |
title_sort |
arh: predicting splice variants from genome-wide data with modified entropy |
topic |
Computational Mathematics Computational Theory and Mathematics Computer Science Applications Molecular Biology Biochemistry Statistics and Probability |
url |
http://dx.doi.org/10.1093/bioinformatics/btp626 |
publishDate |
2010 |
physical |
84-90 |
description |
<jats:title>Abstract</jats:title>
<jats:p>Motivation: Exon arrays allow the quantitative study of alternative splicing (AS) on a genome-wide scale. A variety of splicing prediction methods has been proposed for Affymetrix exon arrays mainly focusing on geometric correlation measures or analysis of variance. In this article, we introduce an information theoretic concept that is based on modification of the well-known entropy function.</jats:p>
<jats:p>Results: We have developed an AS robust prediction method based on entropy (ARH). We can show that this measure copes with bias inherent in the analysis of AS such as the dependency of prediction performance on the number of exons or variable exon expression. In order to judge the performance of ARH, we have compared it with eight existing splicing prediction methods using experimental benchmark data and demonstrate that ARH is a well-performing new method for the prediction of splice variants.</jats:p>
<jats:p>Availability and Implementation: ARH is implemented in R and provided in the Supplementary Material.</jats:p>
<jats:p>Contact: rasche@molgen.mpg.de</jats:p>
<jats:p>Supplementary information: Supplementary data are available at Bioinformatics online.</jats:p> |
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author | Rasche, Axel, Herwig, Ralf |
author_facet | Rasche, Axel, Herwig, Ralf, Rasche, Axel, Herwig, Ralf |
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description | <jats:title>Abstract</jats:title> <jats:p>Motivation: Exon arrays allow the quantitative study of alternative splicing (AS) on a genome-wide scale. A variety of splicing prediction methods has been proposed for Affymetrix exon arrays mainly focusing on geometric correlation measures or analysis of variance. In this article, we introduce an information theoretic concept that is based on modification of the well-known entropy function.</jats:p> <jats:p>Results: We have developed an AS robust prediction method based on entropy (ARH). We can show that this measure copes with bias inherent in the analysis of AS such as the dependency of prediction performance on the number of exons or variable exon expression. In order to judge the performance of ARH, we have compared it with eight existing splicing prediction methods using experimental benchmark data and demonstrate that ARH is a well-performing new method for the prediction of splice variants.</jats:p> <jats:p>Availability and Implementation: ARH is implemented in R and provided in the Supplementary Material.</jats:p> <jats:p>Contact: rasche@molgen.mpg.de</jats:p> <jats:p>Supplementary information: Supplementary data are available at Bioinformatics online.</jats:p> |
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spelling | Rasche, Axel Herwig, Ralf 1367-4811 1367-4803 Oxford University Press (OUP) Computational Mathematics Computational Theory and Mathematics Computer Science Applications Molecular Biology Biochemistry Statistics and Probability http://dx.doi.org/10.1093/bioinformatics/btp626 <jats:title>Abstract</jats:title> <jats:p>Motivation: Exon arrays allow the quantitative study of alternative splicing (AS) on a genome-wide scale. A variety of splicing prediction methods has been proposed for Affymetrix exon arrays mainly focusing on geometric correlation measures or analysis of variance. In this article, we introduce an information theoretic concept that is based on modification of the well-known entropy function.</jats:p> <jats:p>Results: We have developed an AS robust prediction method based on entropy (ARH). We can show that this measure copes with bias inherent in the analysis of AS such as the dependency of prediction performance on the number of exons or variable exon expression. In order to judge the performance of ARH, we have compared it with eight existing splicing prediction methods using experimental benchmark data and demonstrate that ARH is a well-performing new method for the prediction of splice variants.</jats:p> <jats:p>Availability and Implementation: ARH is implemented in R and provided in the Supplementary Material.</jats:p> <jats:p>Contact: rasche@molgen.mpg.de</jats:p> <jats:p>Supplementary information: Supplementary data are available at Bioinformatics online.</jats:p> ARH: predicting splice variants from genome-wide data with modified entropy Bioinformatics |
spellingShingle | Rasche, Axel, Herwig, Ralf, Bioinformatics, ARH: predicting splice variants from genome-wide data with modified entropy, Computational Mathematics, Computational Theory and Mathematics, Computer Science Applications, Molecular Biology, Biochemistry, Statistics and Probability |
title | ARH: predicting splice variants from genome-wide data with modified entropy |
title_full | ARH: predicting splice variants from genome-wide data with modified entropy |
title_fullStr | ARH: predicting splice variants from genome-wide data with modified entropy |
title_full_unstemmed | ARH: predicting splice variants from genome-wide data with modified entropy |
title_short | ARH: predicting splice variants from genome-wide data with modified entropy |
title_sort | arh: predicting splice variants from genome-wide data with modified entropy |
title_unstemmed | ARH: predicting splice variants from genome-wide data with modified entropy |
topic | Computational Mathematics, Computational Theory and Mathematics, Computer Science Applications, Molecular Biology, Biochemistry, Statistics and Probability |
url | http://dx.doi.org/10.1093/bioinformatics/btp626 |