Completing SBGN-AF networks by logic-based hypothesis finding

Yoshitaka Yamamoto, Adrien Rougny, Hidetomo Nabeshima, Katsumi Inoue, Hisao Moriya, Christine Froidevaux, Koji Iwanuma

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

This study considers formal methods for finding unknown interactions of incomplete molecular networks using microarray profiles. In systems biology, a challenging problem lies in the growing scale and complexity of molecular networks. Along with high-throughput experimental tools, it is not straightforward to reconstruct huge and complicated networks using observed data by hand. Thus, we address the completion problem of our target networks represented by a standard markup language, called SBGN (in particular, Activity Flow). Our proposed method is based on logic-based hypothesis finding techniques; given an input SBGN network and its profile data, missing interactions can be logically generated as hypotheses by the proposed method. In this paper, we also show empirical results that demonstrate how the proposed method works with a real network involved in the glucose repression of S. cerevisiae.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer Verlag
Pages165-179
Number of pages15
Volume8738 LNBI
ISBN (Print)9783319103976, 9783319103976
DOIs
Publication statusPublished - 2014
Event1st International Conference on Formal Methods in Macro-Biology, FMMB 2014 - Noumea, New Caledonia
Duration: Sep 22 2014Sep 24 2014

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8738 LNBI
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other1st International Conference on Formal Methods in Macro-Biology, FMMB 2014
CountryNew Caledonia
CityNoumea
Period9/22/149/24/14

Keywords

  • completion
  • glucose repression
  • hypothesis finding
  • SBGN

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

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  • Cite this

    Yamamoto, Y., Rougny, A., Nabeshima, H., Inoue, K., Moriya, H., Froidevaux, C., & Iwanuma, K. (2014). Completing SBGN-AF networks by logic-based hypothesis finding. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8738 LNBI, pp. 165-179). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8738 LNBI). Springer Verlag. https://doi.org/10.1007/978-3-319-10398-3_14