Protein Sequences Analysis Based on Smoothed PST

Author

Liu, Yong ; Yan, Tuanjun ; Zhang, Rui

Author_Institution

Inst. of Intell. Vision & Image, Inf. China Three Gorges Univ., Yichang, China

fYear

2009

fDate

11-13 June 2009

Firstpage

1

Lastpage

4

Abstract

Sequence alignment algorithms, which are hardly to be efficient, are frequently used in protein sequences analysis. In order to improve the analyzing efficiency, an improved PST(Probabilistic Suffix Trees) model is proposed in this paper. Firstly, by analyzing the similarity between protein sequences analysis and sequences data mining, the idea of using PST model to analyze protein sequences is presented; And then the standard PST model is improved by smoothing operation according to the features of protein sequences analysis; Next, taking the smoothed PST as features of data set, the similarities degree between protein sequences are calculated by using the similarities of the normalized sequences; At last, the effectiveness and high efficiency of the algorithm are verified by some protein sequences analysis examples.

Keywords

biology computing; data mining; molecular biophysics; probability; proteins; smoothing methods; trees (mathematics); data set feature; protein sequences analysis; sequence alignment algorithm; sequence data mining; smoothed probabilistic suffix tree model; Algorithm design and analysis; Data analysis; Data mining; Image analysis; Image sequence analysis; Information analysis; Predictive models; Protein sequence; Smoothing methods; Testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Bioinformatics and Biomedical Engineering , 2009. ICBBE 2009. 3rd International Conference on

Conference_Location

Beijing

Print_ISBN

978-1-4244-2901-1

Electronic_ISBN

978-1-4244-2902-8

Type

conf

DOI

10.1109/ICBBE.2009.5163257

Filename

5163257