Volume 3 Number 1
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

  • Received: 2005-01-28
Fund Project:

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

通讯作者: 陈斌, bchen63@163.com
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    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

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.

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
Citation: 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
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