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The Prediction of Human Genes in DNA Based on a Generalized Hidden Markov Model

  • Rui GuoEmail author
  • Ke Yan
  • Wei He
  • Jian Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9967)

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.

Keywords

Gene prediction WWAM IMM GHMM The prefix sum arrays The method based on similarity weighting of sequence patterns 

Notes

Acknowledgement

This article is partly supported by the Shenzhen Municipal Science and Technology Innovation Council (Nos. JCYJ20140904154645958).

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Bio-Computing Research Center, Shenzhen Graduate SchoolHarbin Institute of TechnologyShenzhenChina

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