Abstract
The Generalized Hidden Markov Model (GHMM) has been proved to be an excellently general probabilistic model of the gene structure of human genomic sequences. It can simultaneously incorporate different signal descriptions like splicing sites and content descriptions, for instance, compositional features of exons and introns. Enjoying its flexibility and convincing probabilistic underpinnings, we integrate some other modification of submodels and then implement a prediction program of Human Genes in DNA. The program has the capacity to predict multiple genes in a sequence, to deal with partial as well as complete genes, and to predict consistent sets of genes occurring on either or both DNA strands. More importantly, it also can perform well for longer sequences with an unknown number of genes in them. In the experiments, the results show that the proposed method has better performance in prediction accuracy than some existing methods, and over 70 % of exons can be identified exactly.
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Acknowledgement
This article is partly supported by the Shenzhen Municipal Science and Technology Innovation Council (Nos. JCYJ20140904154645958).
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Guo, R., Yan, K., He, W., Zhang, J. (2016). The Prediction of Human Genes in DNA Based on a Generalized Hidden Markov Model. In: You, Z., et al. Biometric Recognition. CCBR 2016. Lecture Notes in Computer Science(), vol 9967. Springer, Cham. https://doi.org/10.1007/978-3-319-46654-5_82
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DOI: https://doi.org/10.1007/978-3-319-46654-5_82
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