The statistical approach to biological event extraction using Markov’s method

Wen Juan Hou; Bamfa Ceesay

doi:10.1007/978-3-319-42007-3_18

The statistical approach to biological event extraction using Markov’s method

Wen Juan Hou^*, Bamfa Ceesay

^*Corresponding author for this work

Department of Computer Science and Information Engineering

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

1 Citation (Scopus)

Abstract

Gene Regulation Network (GRN) is a graphical representation of the relationship for a collection of regulators that interact with each other and with other substances in the cell to govern the gene expression levels of mRNA and proteins. In this study, we examine the extraction of GRN from literatures using a statistical method. Markovian logic has been used in the natural language processing domain extensively such as in the field of speech recognition. This paper presents an event extraction approach using the Markov’s method and the logical predicates. An event extraction task is modeled into a Markov’s model using the logical predicates and a set of weighted first ordered formulae that defines a distribution of events over a set of ground atoms of the predicates that is specified using the training and development data. The experimental results has a state-of-the-art F-score comparable 2013 BioNLP shared task and gets 81 % precision in forming the gene regulation network. It shows we have a good performance in solving this problem.

Original language	English
Title of host publication	Trends in Applied Knowledge-Based Systems and Data Science - 29th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2016, Proceedings
Editors	Moonis Ali, Hamido Fujita, Jun Sasaki, Masaki Kurematsu, Ali Selamat
Publisher	Springer Verlag
Pages	207-216
Number of pages	10
ISBN (Print)	9783319420066
DOIs	https://doi.org/10.1007/978-3-319-42007-3_18
Publication status	Published - 2016
Event	29th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2016 - Morioka, Japan Duration: 2016 Aug 2 → 2016 Aug 4

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9799
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	29th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2016
Country/Territory	Japan
City	Morioka
Period	2016/08/02 → 2016/08/04

Keywords

Bayesian network
Biological entity
Biological event
First order logic
Gene regulation network
Markov’s model

ASJC Scopus subject areas

Theoretical Computer Science
Computer Science(all)

Access to Document

10.1007/978-3-319-42007-3_18

Cite this

Hou, W. J., & Ceesay, B. (2016). The statistical approach to biological event extraction using Markov’s method. In M. Ali, H. Fujita, J. Sasaki, M. Kurematsu, & A. Selamat (Eds.), Trends in Applied Knowledge-Based Systems and Data Science - 29th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2016, Proceedings (pp. 207-216). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9799). Springer Verlag. https://doi.org/10.1007/978-3-319-42007-3_18

The statistical approach to biological event extraction using Markov’s method. / Hou, Wen Juan; Ceesay, Bamfa.
Trends in Applied Knowledge-Based Systems and Data Science - 29th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2016, Proceedings. ed. / Moonis Ali; Hamido Fujita; Jun Sasaki; Masaki Kurematsu; Ali Selamat. Springer Verlag, 2016. p. 207-216 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9799).

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

Hou, WJ & Ceesay, B 2016, The statistical approach to biological event extraction using Markov’s method. in M Ali, H Fujita, J Sasaki, M Kurematsu & A Selamat (eds), Trends in Applied Knowledge-Based Systems and Data Science - 29th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2016, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9799, Springer Verlag, pp. 207-216, 29th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2016, Morioka, Japan, 2016/08/02. https://doi.org/10.1007/978-3-319-42007-3_18

Hou WJ, Ceesay B. The statistical approach to biological event extraction using Markov’s method. In Ali M, Fujita H, Sasaki J, Kurematsu M, Selamat A, editors, Trends in Applied Knowledge-Based Systems and Data Science - 29th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2016, Proceedings. Springer Verlag. 2016. p. 207-216. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-42007-3_18

Hou, Wen Juan ; Ceesay, Bamfa. / The statistical approach to biological event extraction using Markov’s method. Trends in Applied Knowledge-Based Systems and Data Science - 29th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2016, Proceedings. editor / Moonis Ali ; Hamido Fujita ; Jun Sasaki ; Masaki Kurematsu ; Ali Selamat. Springer Verlag, 2016. pp. 207-216 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{f211955ac77e48c4a65a910c5116c224,

title = "The statistical approach to biological event extraction using Markov{\textquoteright}s method",

abstract = "Gene Regulation Network (GRN) is a graphical representation of the relationship for a collection of regulators that interact with each other and with other substances in the cell to govern the gene expression levels of mRNA and proteins. In this study, we examine the extraction of GRN from literatures using a statistical method. Markovian logic has been used in the natural language processing domain extensively such as in the field of speech recognition. This paper presents an event extraction approach using the Markov{\textquoteright}s method and the logical predicates. An event extraction task is modeled into a Markov{\textquoteright}s model using the logical predicates and a set of weighted first ordered formulae that defines a distribution of events over a set of ground atoms of the predicates that is specified using the training and development data. The experimental results has a state-of-the-art F-score comparable 2013 BioNLP shared task and gets 81 % precision in forming the gene regulation network. It shows we have a good performance in solving this problem.",

keywords = "Bayesian network, Biological entity, Biological event, First order logic, Gene regulation network, Markov{\textquoteright}s model",

author = "Hou, {Wen Juan} and Bamfa Ceesay",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2016.; 29th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2016 ; Conference date: 02-08-2016 Through 04-08-2016",

year = "2016",

doi = "10.1007/978-3-319-42007-3_18",

language = "English",

isbn = "9783319420066",

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

publisher = "Springer Verlag",

pages = "207--216",

editor = "Moonis Ali and Hamido Fujita and Jun Sasaki and Masaki Kurematsu and Ali Selamat",

booktitle = "Trends in Applied Knowledge-Based Systems and Data Science - 29th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2016, Proceedings",

}

TY - GEN

T1 - The statistical approach to biological event extraction using Markov’s method

AU - Hou, Wen Juan

AU - Ceesay, Bamfa

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

PY - 2016

Y1 - 2016

N2 - Gene Regulation Network (GRN) is a graphical representation of the relationship for a collection of regulators that interact with each other and with other substances in the cell to govern the gene expression levels of mRNA and proteins. In this study, we examine the extraction of GRN from literatures using a statistical method. Markovian logic has been used in the natural language processing domain extensively such as in the field of speech recognition. This paper presents an event extraction approach using the Markov’s method and the logical predicates. An event extraction task is modeled into a Markov’s model using the logical predicates and a set of weighted first ordered formulae that defines a distribution of events over a set of ground atoms of the predicates that is specified using the training and development data. The experimental results has a state-of-the-art F-score comparable 2013 BioNLP shared task and gets 81 % precision in forming the gene regulation network. It shows we have a good performance in solving this problem.

AB - Gene Regulation Network (GRN) is a graphical representation of the relationship for a collection of regulators that interact with each other and with other substances in the cell to govern the gene expression levels of mRNA and proteins. In this study, we examine the extraction of GRN from literatures using a statistical method. Markovian logic has been used in the natural language processing domain extensively such as in the field of speech recognition. This paper presents an event extraction approach using the Markov’s method and the logical predicates. An event extraction task is modeled into a Markov’s model using the logical predicates and a set of weighted first ordered formulae that defines a distribution of events over a set of ground atoms of the predicates that is specified using the training and development data. The experimental results has a state-of-the-art F-score comparable 2013 BioNLP shared task and gets 81 % precision in forming the gene regulation network. It shows we have a good performance in solving this problem.

KW - Bayesian network

KW - Biological entity

KW - Biological event

KW - First order logic

KW - Gene regulation network

KW - Markov’s model

UR - http://www.scopus.com/inward/record.url?scp=84978821859&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84978821859&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-42007-3_18

DO - 10.1007/978-3-319-42007-3_18

M3 - Conference contribution

AN - SCOPUS:84978821859

SN - 9783319420066

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

SP - 207

EP - 216

BT - Trends in Applied Knowledge-Based Systems and Data Science - 29th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2016, Proceedings

A2 - Ali, Moonis

A2 - Fujita, Hamido

A2 - Sasaki, Jun

A2 - Kurematsu, Masaki

A2 - Selamat, Ali

PB - Springer Verlag

T2 - 29th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2016

Y2 - 2 August 2016 through 4 August 2016

ER -

The statistical approach to biological event extraction using Markov’s method

Abstract

Publication series

Other

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this