The prediction of human genes in DNA based on a generalized hidden Markov model

Rui Guo; Ke Yan; Wei He; Jian Zhang

doi:10.1007/978-3-319-46654-5_82

The prediction of human genes in DNA based on a generalized hidden Markov model

Rui Guo^*, Ke Yan, Wei He, Jian Zhang

^*Corresponding author for this work

Harbin Institute of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Citation (Scopus)

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.

Original language	English
Title of host publication	Biometric Recognition - 11th Chinese Conference, CCBR 2016, Proceedings
Editors	Shiguang Shan, Zhisheng You, Jie Zhou, Weishi Zheng, Yunhong Wang, Zhenan Sun, Jianjiang Feng, Qijun Zhao
Publisher	Springer Verlag
Pages	747-755
Number of pages	9
ISBN (Print)	9783319466538
DOIs	https://doi.org/10.1007/978-3-319-46654-5_82
Publication status	Published - 2016
Externally published	Yes
Event	11th Chinese Conference on Biometric Recognition, CCBR 2016 - Chengdu, China Duration: 14 Oct 2016 → 16 Oct 2016

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9967 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	11th Chinese Conference on Biometric Recognition, CCBR 2016
Country/Territory	China
City	Chengdu
Period	14/10/16 → 16/10/16

Keywords

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

Access to Document

10.1007/978-3-319-46654-5_82

Cite this

Guo, R., Yan, K., He, W., & Zhang, J. (2016). The prediction of human genes in DNA based on a generalized hidden Markov model. In S. Shan, Z. You, J. Zhou, W. Zheng, Y. Wang, Z. Sun, J. Feng, & Q. Zhao (Eds.), Biometric Recognition - 11th Chinese Conference, CCBR 2016, Proceedings (pp. 747-755). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9967 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-46654-5_82

Guo, Rui ; Yan, Ke ; He, Wei et al. / The prediction of human genes in DNA based on a generalized hidden Markov model. Biometric Recognition - 11th Chinese Conference, CCBR 2016, Proceedings. editor / Shiguang Shan ; Zhisheng You ; Jie Zhou ; Weishi Zheng ; Yunhong Wang ; Zhenan Sun ; Jianjiang Feng ; Qijun Zhao. Springer Verlag, 2016. pp. 747-755 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "The prediction of human genes in DNA based on a generalized hidden Markov model",

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

author = "Rui Guo and Ke Yan and Wei He and Jian Zhang",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2016.; 11th Chinese Conference on Biometric Recognition, CCBR 2016 ; Conference date: 14-10-2016 Through 16-10-2016",

year = "2016",

doi = "10.1007/978-3-319-46654-5_82",

language = "English",

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series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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editor = "Shiguang Shan and Zhisheng You and Jie Zhou and Weishi Zheng and Yunhong Wang and Zhenan Sun and Jianjiang Feng and Qijun Zhao",

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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 S Shan, Z You, J Zhou, W Zheng, Y Wang, Z Sun, J Feng & Q Zhao (eds), Biometric Recognition - 11th Chinese Conference, CCBR 2016, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9967 LNCS, Springer Verlag, pp. 747-755, 11th Chinese Conference on Biometric Recognition, CCBR 2016, Chengdu, China, 14/10/16. https://doi.org/10.1007/978-3-319-46654-5_82

The prediction of human genes in DNA based on a generalized hidden Markov model. / Guo, Rui; Yan, Ke; He, Wei et al.
Biometric Recognition - 11th Chinese Conference, CCBR 2016, Proceedings. ed. / Shiguang Shan; Zhisheng You; Jie Zhou; Weishi Zheng; Yunhong Wang; Zhenan Sun; Jianjiang Feng; Qijun Zhao. Springer Verlag, 2016. p. 747-755 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9967 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - The prediction of human genes in DNA based on a generalized hidden Markov model

AU - Guo, Rui

AU - Yan, Ke

AU - He, Wei

AU - Zhang, Jian

N1 - Publisher Copyright: © Springer International Publishing AG 2016.

PY - 2016

Y1 - 2016

N2 - 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.

AB - 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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KW - Gene prediction

KW - IMM

KW - The method based on similarity weighting of sequence patterns

KW - The prefix sum arrays

KW - WWAM

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M3 - Conference contribution

AN - SCOPUS:84992445753

SN - 9783319466538

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 747

EP - 755

BT - Biometric Recognition - 11th Chinese Conference, CCBR 2016, Proceedings

A2 - Shan, Shiguang

A2 - You, Zhisheng

A2 - Zhou, Jie

A2 - Zheng, Weishi

A2 - Wang, Yunhong

A2 - Sun, Zhenan

A2 - Feng, Jianjiang

A2 - Zhao, Qijun

PB - Springer Verlag

T2 - 11th Chinese Conference on Biometric Recognition, CCBR 2016

Y2 - 14 October 2016 through 16 October 2016

ER -

Guo R, Yan K, He W, Zhang J. The prediction of human genes in DNA based on a generalized hidden Markov model. In Shan S, You Z, Zhou J, Zheng W, Wang Y, Sun Z, Feng J, Zhao Q, editors, Biometric Recognition - 11th Chinese Conference, CCBR 2016, Proceedings. Springer Verlag. 2016. p. 747-755. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-46654-5_82

The prediction of human genes in DNA based on a generalized hidden Markov model

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