Identifying gene regulatory networks from experimental data

Ting Chen; Vladimir Filkov; Steven S. Skiena

doi:10.1145/299432.299462

Identifying gene regulatory networks from experimental data

Ting Chen
, Vladimir Filkov
, Steven S. Skiena

Computer Science

Stony Brook University

Harvard University

Research output: Contribution to conference › Paper › peer-review

66 Scopus citations

Abstract

Identifying gene regulatory networks from experimental data is an area of extremely active research. Associating functions to genes based on a huge amount of data is an important and challenging problem. A methodology is proposed to analyze large, multiple time-series data sets measuring the expression level of different genes for Saccharomyces cerevisiae. A simulated annealing-based optimizer is employed to provide the maximum flexibility in the prototype system. Several algorithmic and complexity results for the gene regulation problem are discussed.

Original language	English
Pages	94-103
Number of pages	10
DOIs	https://doi.org/10.1145/299432.299462
State	Published - 1999
Event	Proceedings of the 1999 3rd Annual International Conference on Computational Molecular Biology, RECOMB '99 - Lyon Duration: Apr 11 1999 → Apr 14 1999

Conference

Conference	Proceedings of the 1999 3rd Annual International Conference on Computational Molecular Biology, RECOMB '99
City	Lyon
Period	04/11/99 → 04/14/99

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10.1145/299432.299462

Cite this

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title = "Identifying gene regulatory networks from experimental data",

abstract = "Identifying gene regulatory networks from experimental data is an area of extremely active research. Associating functions to genes based on a huge amount of data is an important and challenging problem. A methodology is proposed to analyze large, multiple time-series data sets measuring the expression level of different genes for Saccharomyces cerevisiae. A simulated annealing-based optimizer is employed to provide the maximum flexibility in the prototype system. Several algorithmic and complexity results for the gene regulation problem are discussed.",

author = "Ting Chen and Vladimir Filkov and Skiena, \{Steven S.\}",

year = "1999",

doi = "10.1145/299432.299462",

language = "English",

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note = "Proceedings of the 1999 3rd Annual International Conference on Computational Molecular Biology, RECOMB '99 ; Conference date: 11-04-1999 Through 14-04-1999",

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AU - Chen, Ting

AU - Filkov, Vladimir

AU - Skiena, Steven S.

PY - 1999

Y1 - 1999

N2 - Identifying gene regulatory networks from experimental data is an area of extremely active research. Associating functions to genes based on a huge amount of data is an important and challenging problem. A methodology is proposed to analyze large, multiple time-series data sets measuring the expression level of different genes for Saccharomyces cerevisiae. A simulated annealing-based optimizer is employed to provide the maximum flexibility in the prototype system. Several algorithmic and complexity results for the gene regulation problem are discussed.

AB - Identifying gene regulatory networks from experimental data is an area of extremely active research. Associating functions to genes based on a huge amount of data is an important and challenging problem. A methodology is proposed to analyze large, multiple time-series data sets measuring the expression level of different genes for Saccharomyces cerevisiae. A simulated annealing-based optimizer is employed to provide the maximum flexibility in the prototype system. Several algorithmic and complexity results for the gene regulation problem are discussed.

UR - https://www.scopus.com/pages/publications/0032668662

U2 - 10.1145/299432.299462

DO - 10.1145/299432.299462

M3 - Paper

SP - 94

EP - 103

T2 - Proceedings of the 1999 3rd Annual International Conference on Computational Molecular Biology, RECOMB '99

Y2 - 11 April 1999 through 14 April 1999

ER -

Identifying gene regulatory networks from experimental data

Abstract

Conference

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