Application of hidden Markov models to gene prediction in DNA

Document Type

Conference Proceeding

Publication Date

1-1-1999

Abstract

Programs currently available for gene prediction from within genomic DNA are far from being powerful enough to elucidate the gene structure completely. We develop a hidden Markov model (HMM) to represent the degeneracy features of splicing junction donor sites in eucaryotic genes. The HMM system is fully trained using an expectation maximization algorithm and the system performance is evaluated using the 10-way cross-validation method. Experimental results show that our HMM system can correctly classify more than 95% of the candidate sequences into the right categories. More than 91% of the true donor sites and 97% of the false donor sites in the test data are classified correctly. These results are very promising, considering that only the local information in DNA is used. This model will be a very important component of effective and accurate gene structure detection system currently being developed in our lab.

Identifier

84879914535 (Scopus)

ISBN

[0769504469, 9780769504469]

Publication Title

Proceedings 1999 International Conference on Information Intelligence and Systems Iciis 1999

External Full Text Location

https://doi.org/10.1109/ICIIS.1999.810222

First Page

40

Last Page

47

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