Sparse Gaussian graphical model with missing values

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

    1 Citation (Scopus)


    Recent advances in measurement technology have enabled us to measure various omic layers, such as genome, transcriptome, proteome, and metabolome layers. The demand for data analysis to determine the network structure of the interaction between molecular species is increasing. The Gaussian graphical model is one method of estimating the network structure. However, biological omics data sets tend to include missing values, which is conventionally handled by preprocessing. We propose a novel method by which to estimate the network structure together with missing values by combining a sparse graphical model and matrix factorization. The proposed method was validated by artificial data sets and was applied to a signal transduction data set as a test run.

    Original languageEnglish
    Title of host publicationProceedings of the 21st Conference of Open Innovations Association, FRUCT 2017
    PublisherIEEE Computer Society
    Number of pages8
    ISBN (Electronic)9789526865324
    Publication statusPublished - Jul 1 2017
    Event21st Conference of Open Innovations Association, FRUCT 2017 - Helsinki, Finland
    Duration: Nov 6 2017Nov 10 2017

    Publication series

    NameConference of Open Innovation Association, FRUCT
    ISSN (Print)2305-7254


    Other21st Conference of Open Innovations Association, FRUCT 2017

    All Science Journal Classification (ASJC) codes

    • Computer Science(all)
    • Electrical and Electronic Engineering


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