A new approach to gene prediction using the self-organizing map

Shaun A. Mahony; T. J. Smith; J. O. McInerney; A. Golden

doi:10.1109/CSB.2003.1227365

A new approach to gene prediction using the self-organizing map

Shaun A. Mahony, T. J. Smith, J. O. McInerney, A. Golden

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

In this poster we present a gene prediction approach based on the self-organizing map that has the ability to automatically identify all the major patterns of content variation within a genome. The genome may then be scanned for regions displaying the same properties as one of these automatically identified models. Even using a relatively simple coding measure (codon usage), this method can predict the location of protein-coding sequences with a reasonably high accuracy. We also show other advantages of the approach, such as the ability to indicate genes that contain frame-shifts. We believe that this method has the potential to become a useful addition to the genome annotation process.

Original language	English (US)
Title of host publication	Proceedings of the 2003 IEEE Bioinformatics Conference, CSB 2003
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	444-445
Number of pages	2
ISBN (Electronic)	0769520006, 9780769520001
DOIs	https://doi.org/10.1109/CSB.2003.1227365
State	Published - Jan 1 2003
Event	2nd International IEEE Computer Society Computational Systems Bioinformatics Conference, CSB 2003 - Stanford, United States Duration: Aug 11 2003 → Aug 14 2003

Publication series

Name	Proceedings of the 2003 IEEE Bioinformatics Conference, CSB 2003

Other

Other	2nd International IEEE Computer Society Computational Systems Bioinformatics Conference, CSB 2003
Country/Territory	United States
City	Stanford
Period	8/11/03 → 8/14/03

All Science Journal Classification (ASJC) codes

Electrical and Electronic Engineering
Computer Science Applications

Access to Document

10.1109/CSB.2003.1227365

Cite this

Mahony, S. A., Smith, T. J., McInerney, J. O., & Golden, A. (2003). A new approach to gene prediction using the self-organizing map. In Proceedings of the 2003 IEEE Bioinformatics Conference, CSB 2003 (pp. 444-445). Article 1227365 (Proceedings of the 2003 IEEE Bioinformatics Conference, CSB 2003). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CSB.2003.1227365

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title = "A new approach to gene prediction using the self-organizing map",

abstract = "In this poster we present a gene prediction approach based on the self-organizing map that has the ability to automatically identify all the major patterns of content variation within a genome. The genome may then be scanned for regions displaying the same properties as one of these automatically identified models. Even using a relatively simple coding measure (codon usage), this method can predict the location of protein-coding sequences with a reasonably high accuracy. We also show other advantages of the approach, such as the ability to indicate genes that contain frame-shifts. We believe that this method has the potential to become a useful addition to the genome annotation process.",

author = "Mahony, {Shaun A.} and Smith, {T. J.} and McInerney, {J. O.} and A. Golden",

year = "2003",

month = jan,

day = "1",

doi = "10.1109/CSB.2003.1227365",

language = "English (US)",

series = "Proceedings of the 2003 IEEE Bioinformatics Conference, CSB 2003",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "444--445",

booktitle = "Proceedings of the 2003 IEEE Bioinformatics Conference, CSB 2003",

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note = "2nd International IEEE Computer Society Computational Systems Bioinformatics Conference, CSB 2003 ; Conference date: 11-08-2003 Through 14-08-2003",

}

Mahony, SA, Smith, TJ, McInerney, JO & Golden, A 2003, A new approach to gene prediction using the self-organizing map. in Proceedings of the 2003 IEEE Bioinformatics Conference, CSB 2003., 1227365, Proceedings of the 2003 IEEE Bioinformatics Conference, CSB 2003, Institute of Electrical and Electronics Engineers Inc., pp. 444-445, 2nd International IEEE Computer Society Computational Systems Bioinformatics Conference, CSB 2003, Stanford, United States, 8/11/03. https://doi.org/10.1109/CSB.2003.1227365

A new approach to gene prediction using the self-organizing map. / Mahony, Shaun A.; Smith, T. J.; McInerney, J. O. et al.
Proceedings of the 2003 IEEE Bioinformatics Conference, CSB 2003. Institute of Electrical and Electronics Engineers Inc., 2003. p. 444-445 1227365 (Proceedings of the 2003 IEEE Bioinformatics Conference, CSB 2003).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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T1 - A new approach to gene prediction using the self-organizing map

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N2 - In this poster we present a gene prediction approach based on the self-organizing map that has the ability to automatically identify all the major patterns of content variation within a genome. The genome may then be scanned for regions displaying the same properties as one of these automatically identified models. Even using a relatively simple coding measure (codon usage), this method can predict the location of protein-coding sequences with a reasonably high accuracy. We also show other advantages of the approach, such as the ability to indicate genes that contain frame-shifts. We believe that this method has the potential to become a useful addition to the genome annotation process.

AB - In this poster we present a gene prediction approach based on the self-organizing map that has the ability to automatically identify all the major patterns of content variation within a genome. The genome may then be scanned for regions displaying the same properties as one of these automatically identified models. Even using a relatively simple coding measure (codon usage), this method can predict the location of protein-coding sequences with a reasonably high accuracy. We also show other advantages of the approach, such as the ability to indicate genes that contain frame-shifts. We believe that this method has the potential to become a useful addition to the genome annotation process.

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A new approach to gene prediction using the self-organizing map

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