Sparse Gaussian graphical model with missing values

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


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 - Jan 8 2018
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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