Comparison of computational methods for the identification of cell cycle-regulated genes

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Standard

Comparison of computational methods for the identification of cell cycle-regulated genes. / de Lichtenberg, Ulrik; Jensen, Lars Juhl; Fausbøll, Anders; Jensen, Thomas Skøt; Bork, Peer; Brunak, Søren.

In: Bioinformatics, Vol. 21, No. 7, 2005, p. 1164-71.

Research output: Contribution to journalJournal articleResearchpeer-review

Harvard

de Lichtenberg, U, Jensen, LJ, Fausbøll, A, Jensen, TS, Bork, P & Brunak, S 2005, 'Comparison of computational methods for the identification of cell cycle-regulated genes', Bioinformatics, vol. 21, no. 7, pp. 1164-71. https://doi.org/10.1093/bioinformatics/bti093

APA

de Lichtenberg, U., Jensen, L. J., Fausbøll, A., Jensen, T. S., Bork, P., & Brunak, S. (2005). Comparison of computational methods for the identification of cell cycle-regulated genes. Bioinformatics, 21(7), 1164-71. https://doi.org/10.1093/bioinformatics/bti093

Vancouver

de Lichtenberg U, Jensen LJ, Fausbøll A, Jensen TS, Bork P, Brunak S. Comparison of computational methods for the identification of cell cycle-regulated genes. Bioinformatics. 2005;21(7):1164-71. https://doi.org/10.1093/bioinformatics/bti093

Author

de Lichtenberg, Ulrik ; Jensen, Lars Juhl ; Fausbøll, Anders ; Jensen, Thomas Skøt ; Bork, Peer ; Brunak, Søren. / Comparison of computational methods for the identification of cell cycle-regulated genes. In: Bioinformatics. 2005 ; Vol. 21, No. 7. pp. 1164-71.

Bibtex

@article{a6aceb9563ee4e8f861a48bc4b3aa0b4,
title = "Comparison of computational methods for the identification of cell cycle-regulated genes",
abstract = "MOTIVATION: DNA microarrays have been used extensively to study the cell cycle transcription programme in a number of model organisms. The Saccharomyces cerevisiae data in particular have been subjected to a wide range of bioinformatics analysis methods, aimed at identifying the correct and complete set of periodically expressed genes. RESULTS: Here, we provide the first thorough benchmark of such methods, surprisingly revealing that most new and more mathematically advanced methods actually perform worse than the analysis published with the original microarray data sets. We show that this loss of accuracy specifically affects methods that only model the shape of the expression profile without taking into account the magnitude of regulation. We present a simple permutation-based method that performs better than most existing methods.",
author = "{de Lichtenberg}, Ulrik and Jensen, {Lars Juhl} and Anders Fausb{\o}ll and Jensen, {Thomas Sk{\o}t} and Peer Bork and S{\o}ren Brunak",
year = "2005",
doi = "10.1093/bioinformatics/bti093",
language = "English",
volume = "21",
pages = "1164--71",
journal = "Bioinformatics",
issn = "1367-4803",
publisher = "Oxford University Press",
number = "7",

}

RIS

TY - JOUR

T1 - Comparison of computational methods for the identification of cell cycle-regulated genes

AU - de Lichtenberg, Ulrik

AU - Jensen, Lars Juhl

AU - Fausbøll, Anders

AU - Jensen, Thomas Skøt

AU - Bork, Peer

AU - Brunak, Søren

PY - 2005

Y1 - 2005

N2 - MOTIVATION: DNA microarrays have been used extensively to study the cell cycle transcription programme in a number of model organisms. The Saccharomyces cerevisiae data in particular have been subjected to a wide range of bioinformatics analysis methods, aimed at identifying the correct and complete set of periodically expressed genes. RESULTS: Here, we provide the first thorough benchmark of such methods, surprisingly revealing that most new and more mathematically advanced methods actually perform worse than the analysis published with the original microarray data sets. We show that this loss of accuracy specifically affects methods that only model the shape of the expression profile without taking into account the magnitude of regulation. We present a simple permutation-based method that performs better than most existing methods.

AB - MOTIVATION: DNA microarrays have been used extensively to study the cell cycle transcription programme in a number of model organisms. The Saccharomyces cerevisiae data in particular have been subjected to a wide range of bioinformatics analysis methods, aimed at identifying the correct and complete set of periodically expressed genes. RESULTS: Here, we provide the first thorough benchmark of such methods, surprisingly revealing that most new and more mathematically advanced methods actually perform worse than the analysis published with the original microarray data sets. We show that this loss of accuracy specifically affects methods that only model the shape of the expression profile without taking into account the magnitude of regulation. We present a simple permutation-based method that performs better than most existing methods.

U2 - 10.1093/bioinformatics/bti093

DO - 10.1093/bioinformatics/bti093

M3 - Journal article

C2 - 15513999

VL - 21

SP - 1164

EP - 1171

JO - Bioinformatics

JF - Bioinformatics

SN - 1367-4803

IS - 7

ER -

ID: 40740656