Identification of TSS in the Human Genome Based on a RBF Neural Network

Zhi-Hong Peng; Jie Chen; Li-Jun Cao; Ting-Ting Gao

doi:10.1007/s11633-006-0035-7

January 2006

Article Contents

Zhi-Hong Peng, Jie Chen, Li-Jun Cao and Ting-Ting Gao. Identification of TSS in the Human Genome Based on a RBF Neural Network. International Journal of Automation and Computing, vol. 3, no. 1, pp. 35-40, 2006 doi: 10.1007/s11633-006-0035-7

Cite as: Zhi-Hong Peng, Jie Chen, Li-Jun Cao and Ting-Ting Gao. Identification of TSS in the Human Genome Based on a RBF Neural Network. International Journal of Automation and Computing, vol. 3, no. 1, pp. 35-40, 2006 doi: 10.1007/s11633-006-0035-7

Identification of TSS in the Human Genome Based on a RBF Neural Network

1.
College of Information Science and Technology, Beijing Institute of Technology, Beijing 100081, PRC

Received: 2005-01-28

Fund Project:

This work was supported by the National Natural Science Foundation of China (No.60374069)

Abstract

The identification of functional motifs in a DNA sequence is fundamentally a statistical pattern recognition problem. This paper introduces a new algorithm for the recognition of functional transcription start sites (TSSs) in human genome sequences, in which a RBF neural network is adopted, and an improved heuristic method for a 5-tuple feature viable construction, is proposed and implemented in two RBFPromoter and ImpRBFPromoter packages developed in Visual C++ 6.0. The algorithm is evaluated on several different test sequence sets. Compared with several other promoter recognition programs, this algorithm is proved to be more flexible, with stronger learning ability and higher accuracy.
- Promoter recognition,
- human genome,
- transcription start site,
- RBF neural network

References

[1]	Roderic Guigo.Computational gene identification:an open problem.Computers Chem,vol.21,no.4,pp.215-222,1997.
[2]	James W.Fickett.The gene identification problem:an overview for developers.Computers Chem,vol.20,no.1,pp.103-118,1996.
[3]	Anders Gorm Pedersen,Pierre Baldi,Y.Chauvin.The biology of eukaryotic promoter prediction-a review.Computers& Chemistry,vol.23,no.6,pp.191-207,1999.
[4]	Uwe Ohler,Heinrich Niemann.Identification and analysis of eukaryotic promoters:recent computational approaches.Trends in Genetics,vol.17,no.2,pp.56-60,2001.
[5]	James W.Fickett,Artemis G.Hatzigeorgiou.Eukaryotic Promoter Recognition.Genome Research,vol.7,no.9,pp.861-878,1997.
[6]	Tao Jiang,YingXu,Michael Q.Zhang.Current Topics in Computational Molecular Biology.Tsinghua University press,Beijing,pp.101-255,2002.
[7]	G.B.Hutchinson.The prediction of vertebrate promoter regions using differential hexamer frequency analysis.Bioinformatics,vol.12,no,5,pp.391-398,1996.
[8]	Michael Q.Zhang.Identification of Human Gene Core Promoters in Silico,Genome Research,vol.8,pp.319-326,1997.
[9]	Zhaoqi Bian.Pattern recognition,Tsinghua University Press,second edit,Beijing,pp.250-270,2000.

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1.
沈阳化工大学材料科学与工程学院沈阳 110142

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Identification of TSS in the Human Genome Based on a RBF Neural Network

1. College of Information Science and Technology, Beijing Institute of Technology, Beijing 100081, PRC

Received: 2005-01-28

Fund Project:

This work was supported by the National Natural Science Foundation of China (No.60374069)

Key words:

Abstract: The identification of functional motifs in a DNA sequence is fundamentally a statistical pattern recognition problem. This paper introduces a new algorithm for the recognition of functional transcription start sites (TSSs) in human genome sequences, in which a RBF neural network is adopted, and an improved heuristic method for a 5-tuple feature viable construction, is proposed and implemented in two RBFPromoter and ImpRBFPromoter packages developed in Visual C++ 6.0. The algorithm is evaluated on several different test sequence sets. Compared with several other promoter recognition programs, this algorithm is proved to be more flexible, with stronger learning ability and higher accuracy.

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Reference (9)

[1]	Roderic Guigo.Computational gene identification:an open problem.Computers Chem,vol.21,no.4,pp.215-222,1997.
[2]	James W.Fickett.The gene identification problem:an overview for developers.Computers Chem,vol.20,no.1,pp.103-118,1996.
[3]	Anders Gorm Pedersen,Pierre Baldi,Y.Chauvin.The biology of eukaryotic promoter prediction-a review.Computers& Chemistry,vol.23,no.6,pp.191-207,1999.
[4]	Uwe Ohler,Heinrich Niemann.Identification and analysis of eukaryotic promoters:recent computational approaches.Trends in Genetics,vol.17,no.2,pp.56-60,2001.
[5]	James W.Fickett,Artemis G.Hatzigeorgiou.Eukaryotic Promoter Recognition.Genome Research,vol.7,no.9,pp.861-878,1997.
[6]	Tao Jiang,YingXu,Michael Q.Zhang.Current Topics in Computational Molecular Biology.Tsinghua University press,Beijing,pp.101-255,2002.
[7]	G.B.Hutchinson.The prediction of vertebrate promoter regions using differential hexamer frequency analysis.Bioinformatics,vol.12,no,5,pp.391-398,1996.
[8]	Michael Q.Zhang.Identification of Human Gene Core Promoters in Silico,Genome Research,vol.8,pp.319-326,1997.
[9]	Zhaoqi Bian.Pattern recognition,Tsinghua University Press,second edit,Beijing,pp.250-270,2000.

Identification of TSS in the Human Genome Based on a RBF Neural Network

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