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

Wen-Juan Hou, Bamfa Ceesay

Research output: Chapter in Book/Report/Conference proceedingConference 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 languageEnglish
Title of host publicationTrends 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
EditorsMoonis Ali, Hamido Fujita, Jun Sasaki, Masaki Kurematsu, Ali Selamat
PublisherSpringer Verlag
Pages207-216
Number of pages10
ISBN (Print)9783319420066
DOIs
Publication statusPublished - 2016 Jan 1
Event29th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2016 - Morioka, Japan
Duration: 2016 Aug 22016 Aug 4

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9799
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other29th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2016
CountryJapan
CityMorioka
Period16/8/216/8/4

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)

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