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Application of hidden Markov models to gene prediction in DNA

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2 Author(s)
Yin, M.M. ; Dept. of Comput. Sci., New Jersey Inst. of Technol., Newark, NJ, USA ; Wang, J.T.L.

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

Published in:

Information Intelligence and Systems, 1999. Proceedings. 1999 International Conference on

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