author_facet Rasche, Axel
Herwig, Ralf
Rasche, Axel
Herwig, Ralf
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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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
author_sort rasche, axel
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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