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

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• 关键词：Gene prediction ; WWAM ; IMM ; GHMM ; The prefix sum arrays ; The method based on similarity weighting of sequence patterns
• 刊名：Lecture Notes in Computer Science
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：9967
• 期：1
• 页码：747-755
• 全文大小：918 KB
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• 作者单位：Rui Guo (21)
  Ke Yan (21)
  Wei He (21)
  Jian Zhang (21)
  
  21. Bio-Computing Research Center, Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen, China
  
• 丛书名：Biometric Recognition
• ISBN：978-3-319-46654-5
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349
• 卷排序：9967

文摘

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.