Event extraction for gene regulation network using syntactic and semantic approaches

Wen Juan Hou; Bamfa Ceesay

doi:10.1007/978-3-319-19066-2_54

Event extraction for gene regulation network using syntactic and semantic approaches

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

5 Citations (Scopus)

Abstract

Gene Regulation Network (GRN) is a graphical representation of the relationship between molecular mechanisms and cellular behavior in system biology. This paper examines the extraction of GRN from biological literatures using text mining techniques. The study proposes two independent methods first, a syntactic method and a semantic method in text mining, to extract biological events from the unstructured text. The paper presents the performance of the two methods and then experiments with the combined strategy to construct a gene regulation network from texts. The results show that the graph-based approach obtains a better result on event extraction and produces a much better regulation network than the semantic analysis method. The combination of the two approaches has yet a much slightly better result than that with the individual approach. This exhilarates us to find more future directions in the biological event extraction research.

Original language	English
Title of host publication	Current Approaches in Applied Artificial Intelligence - 28th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2015, Proceedings
Editors	Chang-Hwan Lee, Yongdai Kim, Young Sig Kwon, Juntae Kim, Moonis Ali
Publisher	Springer Verlag
Pages	559-570
Number of pages	12
ISBN (Print)	9783319190655
DOIs	https://doi.org/10.1007/978-3-319-19066-2_54
Publication status	Published - 2015
Event	28th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2015 - Seoul, Korea, Republic of Duration: 2015 Jun 10 → 2015 Jun 12

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9101
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	28th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2015
Country/Territory	Korea, Republic of
City	Seoul
Period	2015/06/10 → 2015/06/12

Keywords

Biological event extraction
Gene regulation network
Graph-based approach
Semantic approach
Slot error rate

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-319-19066-2_54

Cite this

Hou, W. J., & Ceesay, B. (2015). Event extraction for gene regulation network using syntactic and semantic approaches. In C.-H. Lee, Y. Kim, Y. S. Kwon, J. Kim, & M. Ali (Eds.), Current Approaches in Applied Artificial Intelligence - 28th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2015, Proceedings (pp. 559-570). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9101). Springer Verlag. https://doi.org/10.1007/978-3-319-19066-2_54

Event extraction for gene regulation network using syntactic and semantic approaches. / Hou, Wen Juan; Ceesay, Bamfa.
Current Approaches in Applied Artificial Intelligence - 28th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2015, Proceedings. ed. / Chang-Hwan Lee; Yongdai Kim; Young Sig Kwon; Juntae Kim; Moonis Ali. Springer Verlag, 2015. p. 559-570 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9101).

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

Hou, WJ & Ceesay, B 2015, Event extraction for gene regulation network using syntactic and semantic approaches. in C-H Lee, Y Kim, YS Kwon, J Kim & M Ali (eds), Current Approaches in Applied Artificial Intelligence - 28th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2015, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9101, Springer Verlag, pp. 559-570, 28th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2015, Seoul, Korea, Republic of, 2015/06/10. https://doi.org/10.1007/978-3-319-19066-2_54

Hou WJ, Ceesay B. Event extraction for gene regulation network using syntactic and semantic approaches. In Lee CH, Kim Y, Kwon YS, Kim J, Ali M, editors, Current Approaches in Applied Artificial Intelligence - 28th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2015, Proceedings. Springer Verlag. 2015. p. 559-570. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-19066-2_54

Hou, Wen Juan ; Ceesay, Bamfa. / Event extraction for gene regulation network using syntactic and semantic approaches. Current Approaches in Applied Artificial Intelligence - 28th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2015, Proceedings. editor / Chang-Hwan Lee ; Yongdai Kim ; Young Sig Kwon ; Juntae Kim ; Moonis Ali. Springer Verlag, 2015. pp. 559-570 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "Gene Regulation Network (GRN) is a graphical representation of the relationship between molecular mechanisms and cellular behavior in system biology. This paper examines the extraction of GRN from biological literatures using text mining techniques. The study proposes two independent methods first, a syntactic method and a semantic method in text mining, to extract biological events from the unstructured text. The paper presents the performance of the two methods and then experiments with the combined strategy to construct a gene regulation network from texts. The results show that the graph-based approach obtains a better result on event extraction and produces a much better regulation network than the semantic analysis method. The combination of the two approaches has yet a much slightly better result than that with the individual approach. This exhilarates us to find more future directions in the biological event extraction research.",

keywords = "Biological event extraction, Gene regulation network, Graph-based approach, Semantic approach, Slot error rate",

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

note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2015.; 28th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2015 ; Conference date: 10-06-2015 Through 12-06-2015",

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doi = "10.1007/978-3-319-19066-2_54",

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AU - Hou, Wen Juan

AU - Ceesay, Bamfa

N1 - Publisher Copyright: © Springer International Publishing Switzerland 2015.

PY - 2015

Y1 - 2015

N2 - Gene Regulation Network (GRN) is a graphical representation of the relationship between molecular mechanisms and cellular behavior in system biology. This paper examines the extraction of GRN from biological literatures using text mining techniques. The study proposes two independent methods first, a syntactic method and a semantic method in text mining, to extract biological events from the unstructured text. The paper presents the performance of the two methods and then experiments with the combined strategy to construct a gene regulation network from texts. The results show that the graph-based approach obtains a better result on event extraction and produces a much better regulation network than the semantic analysis method. The combination of the two approaches has yet a much slightly better result than that with the individual approach. This exhilarates us to find more future directions in the biological event extraction research.

AB - Gene Regulation Network (GRN) is a graphical representation of the relationship between molecular mechanisms and cellular behavior in system biology. This paper examines the extraction of GRN from biological literatures using text mining techniques. The study proposes two independent methods first, a syntactic method and a semantic method in text mining, to extract biological events from the unstructured text. The paper presents the performance of the two methods and then experiments with the combined strategy to construct a gene regulation network from texts. The results show that the graph-based approach obtains a better result on event extraction and produces a much better regulation network than the semantic analysis method. The combination of the two approaches has yet a much slightly better result than that with the individual approach. This exhilarates us to find more future directions in the biological event extraction research.

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Event extraction for gene regulation network using syntactic and semantic approaches

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