DocumentCode :
2098007
Title :
Splice Sites Detection by Combining Markov and Hidden Markov Model
Author :
Zhang, Quanwei ; Peng, Qinke ; Li, Kankan ; Kang, Xuejiao ; Li, Jing
Author_Institution :
State Key Lab. for Manuf. Syst. Eng., Xi´´an Jiaotong Univ., Xi´´an, China
fYear :
2009
fDate :
17-19 Oct. 2009
Firstpage :
1
Lastpage :
5
Abstract :
Splice sites detection is helpful to the analysis of gene structure and contributes to the prediction of gene products, so it is one of the most important topics in bioinformatics. With the rapid increase of biological data, faster splice sites detecting methods are more appreciated. Markov model and hidden Markov model not only are low-time-cost, but also easy to understand. Besides the detecting result, we can often obtain some biological features from the models, so they are widely used in DNA sites detection. However, the detecting accuracy of the conventional Markov and hidden Markov model is not satisfactory, so in order to improve their detecting accuracy, we propose a dinucleotide-based hidden Markov model, and then combine the Markov model with the dinucleotide-based hidden Markov model to construct an ensemble model. The experiment results show that our models can further improve the detecting accuracy than those of the conventional models and some present models.
Keywords :
DNA; bioinformatics; genomics; hidden Markov models; molecular biophysics; DNA site detecting accuracy; DNA site detection; Markov process; bioinformatics; dinucleotide based hidden Markov model; ensemble model; gene product prediction; gene structure analysis; splice site detection; splice sites detecting method; Biological system modeling; DNA; Hidden Markov models; Information analysis; Laboratories; Manufacturing systems; Neural networks; Sequences; Support vector machines; Systems engineering and theory;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Biomedical Engineering and Informatics, 2009. BMEI '09. 2nd International Conference on
Conference_Location :
Tianjin
Print_ISBN :
978-1-4244-4132-7
Electronic_ISBN :
978-1-4244-4134-1
Type :
conf
DOI :
10.1109/BMEI.2009.5301970
Filename :
5301970
Link To Document :
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