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Error probabilities for local extrema in gene expression data

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Title: Error probabilities for local extrema in gene expression data
Authors: Groot, Perry1 perry@cs.ru.nl, Gilissen, Christian2 C.Gilissen@antrg.umcn.nl, Egmont-Petersen, Michael2 M.EgmontPetersen@antrg.umcn.nl
Source: Pattern Recognition Letters. Nov2007, Vol. 28 Issue 15, p2133-2142. 10p.
Subjects: Gene expression, Distribution (Probability theory), Gaussian distribution, Statistical correlation, Genetic regulation
Abstract: Abstract: Current approaches for the prediction of functional relations from gene expression data often do not have a clear methodology for extracting features and are not accompanied by a clear characterisation of their performance in terms of the inherent noise present in such data sets. Without such a characterisation it is unclear how to focus on the most probable functional relations present. In this article, we start from the fundamental theory of scale-space for obtaining features (i.e., local extrema) from gene expression profiles. We show that under the assumption of Gaussian distributed noise, repeatedly measuring a local extrema behaves like a bivariate Gaussian distribution. Furthermore, the error of not re-observing local extrema is phrased in terms of the integral over the tails of this bivariate Gaussian distribution. Using integration techniques developed in the 1950s, we demonstrate how to compute these error probabilities exactly. [Copyright &y& Elsevier]
Copyright of Pattern Recognition Letters is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: <searchLink fieldCode="AR" term="%22Groot%2C+Perry%22">Groot, Perry</searchLink><relatesTo>1</relatesTo><i> perry@cs.ru.nl</i><br /><searchLink fieldCode="AR" term="%22Gilissen%2C+Christian%22">Gilissen, Christian</searchLink><relatesTo>2</relatesTo><i> C.Gilissen@antrg.umcn.nl</i><br /><searchLink fieldCode="AR" term="%22Egmont-Petersen%2C+Michael%22">Egmont-Petersen, Michael</searchLink><relatesTo>2</relatesTo><i> M.EgmontPetersen@antrg.umcn.nl</i>
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  Data: <searchLink fieldCode="JN" term="%22Pattern+Recognition+Letters%22">Pattern Recognition Letters</searchLink>. Nov2007, Vol. 28 Issue 15, p2133-2142. 10p.
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  Data: <searchLink fieldCode="DE" term="%22Gene+expression%22">Gene expression</searchLink><br /><searchLink fieldCode="DE" term="%22Distribution+%28Probability+theory%29%22">Distribution (Probability theory)</searchLink><br /><searchLink fieldCode="DE" term="%22Gaussian+distribution%22">Gaussian distribution</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+correlation%22">Statistical correlation</searchLink><br /><searchLink fieldCode="DE" term="%22Genetic+regulation%22">Genetic regulation</searchLink>
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  Data: Abstract: Current approaches for the prediction of functional relations from gene expression data often do not have a clear methodology for extracting features and are not accompanied by a clear characterisation of their performance in terms of the inherent noise present in such data sets. Without such a characterisation it is unclear how to focus on the most probable functional relations present. In this article, we start from the fundamental theory of scale-space for obtaining features (i.e., local extrema) from gene expression profiles. We show that under the assumption of Gaussian distributed noise, repeatedly measuring a local extrema behaves like a bivariate Gaussian distribution. Furthermore, the error of not re-observing local extrema is phrased in terms of the integral over the tails of this bivariate Gaussian distribution. Using integration techniques developed in the 1950s, we demonstrate how to compute these error probabilities exactly. [Copyright &y& Elsevier]
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  Data: <i>Copyright of Pattern Recognition Letters is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1016/j.patrec.2007.06.017
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      – Code: eng
        Text: English
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      – SubjectFull: Gene expression
        Type: general
      – SubjectFull: Distribution (Probability theory)
        Type: general
      – SubjectFull: Gaussian distribution
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      – SubjectFull: Statistical correlation
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      – SubjectFull: Genetic regulation
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      – TitleFull: Error probabilities for local extrema in gene expression data
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              Text: Nov2007
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              Y: 2007
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