Folia Medica Indonesiana
ISSN 0303-7932
Vol. 46 / No. 1 / Published : 2010-01
Order : 4, and page :24 - 29
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Original Article :
The implementation of hidden markov model for exon prediction on dna-gene plasmodium falciparum
Author :
- Suhartati Agoes*1
- Dadang Gunawan*2
- Sar Sardy*3
- Department of Electrical Engineering, Faculty of Industrial Engineering, Trisakti University
- Department of Electrical Engineering, University of Indonesia
- Department of Electrical Engineering, University of Indonesia
Abstract :
The Hidden Markov Model (HMM) implementation for exon prediction on DNA-gene Plasmodium falciparum has various models based on exon region’s structure in coding sequence (CDS). The increment of state number was done randomly up to 100, by using a backward-forward HMM with some transition and emission components on each state. Based on the gene basic structure on CDS, it is developed two expansion models for increasing the state number. The Viterbi algorithm is used for training process, and both Viterbi and Baum-Welch algorithms are used respectively for testing process. The correlation coefficient (CC) is used as a performance indicator for all expansion models. The result of simulation for the basic structure at state 9, shows that the best average CC value is 0.73 for Viterbi algorithm, and is 0.72 for Baum-Welch algorithm. In the expansion models, it is found that the best average CC value is 0.78 from the first expansion model for both algorithms at stage 100. The average processing time for training is faster at stage 20 and 30, but almost 15~20 times longer at stage 100, while the average processing time for testing by using Baum-Welch algorithm is twice slower than the Viterbi algorithm.
Keyword :
HMM structure, coding sequence, correlation coefficient,
References :
Anastassiou, D,(2001) Genomic signal processing - : IEEE Signal Processing Magazine
Gardner, MJ et. al.,(2002) Genome sequence of the human malaria parasite Plasmodium falciparum - : Nature
Henderson, J, Salzberg, S & Fasman, KH,(1997) Finding genes in dna with a Hidden Markov Model - : Journal of Computational Biology
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