Network completion using dynamic programming and least-squares fitting

Natsu Nakajima, Takeyuki Tamura, Yoshihiro Yamanishi, Katsuhisa Horimoto, Tatsuya Akutsu

    Research output: Contribution to journalArticle

    12 Citations (Scopus)

    Abstract

    We consider the problem of network completion, which is to make the minimum amount of modifications to a given network so that the resulting network is most consistent with the observed data. We employ here a certain type of differential equations as gene regulation rules in a genetic network, gene expression time series data as observed data, and deletions and additions of edges as basic modification operations. In addition, we assume that the numbers of deleted and added edges are specified. For this problem, we present a novel method using dynamic programming and least-squares fitting and show that it outputs a network with the minimum sum squared error in polynomial time if the maximum indegree of the network is bounded by a constant. We also perform computational experiments using both artificially generated and real gene expression time series data.

    Original languageEnglish
    Article number957620
    JournalThe Scientific World Journal
    Volume2012
    DOIs
    Publication statusPublished - Dec 3 2012

    All Science Journal Classification (ASJC) codes

    • Biochemistry, Genetics and Molecular Biology(all)
    • Environmental Science(all)

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  • Cite this

    Nakajima, N., Tamura, T., Yamanishi, Y., Horimoto, K., & Akutsu, T. (2012). Network completion using dynamic programming and least-squares fitting. The Scientific World Journal, 2012, [957620]. https://doi.org/10.1100/2012/957620