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The Statistical Approach to Biological Event Extraction Using Markov’s Method

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Trends in Applied Knowledge-Based Systems and Data Science (IEA/AIE 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9799))

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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.

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Notes

  1. 1.

    https://www.nlm.nih.gov/bsd/policy/structured_abstracts.html.

  2. 2.

    https://en.wikipedia.org/wiki/Gibbs_measure.

  3. 3.

    http://2013.bionlp-st.org/tasks/gene-regulation-network.

  4. 4.

    http://2013.bionlp-st.org/tasks/gene-regulation-network/test-results.

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Authors and Affiliations

  1. Department of Computer Science and Information Engineering, National Taiwan Normal University, No. 88, Section 4, Ting Chou Road, Taipei, 116, Taiwan, ROC

    Wen-Juan Hou & Bamfa Ceesay

Authors
  1. Wen-Juan Hou
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  2. Bamfa Ceesay
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Corresponding author

Correspondence to Wen-Juan Hou .

Editor information

Editors and Affiliations

  1. Iwate Prefectural University , Iwate, Japan

    Hamido Fujita

  2. Department Computer Science, Texas State University, San Marcos, Texas, USA

    Moonis Ali

  3. Universiti Teknologi Malaysis (UTM), Bahru, Malaysia

    Ali Selamat

  4. Iwate Prefectural University , Iwate, Japan

    Jun Sasaki

  5. Iwate Prefectural University , Iwate, Japan

    Masaki Kurematsu

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Hou, WJ., Ceesay, B. (2016). The Statistical Approach to Biological Event Extraction Using Markov’s Method. In: Fujita, H., Ali, M., Selamat, A., Sasaki, J., Kurematsu, M. (eds) Trends in Applied Knowledge-Based Systems and Data Science. IEA/AIE 2016. Lecture Notes in Computer Science(), vol 9799. Springer, Cham. https://doi.org/10.1007/978-3-319-42007-3_18

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  • DOI: https://doi.org/10.1007/978-3-319-42007-3_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-42006-6

  • Online ISBN: 978-3-319-42007-3

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