Sparse Gaussian graphical model with missing values

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

抜粋

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.

元の言語英語
ホスト出版物のタイトルProceedings of the 21st Conference of Open Innovations Association, FRUCT 2017
出版者IEEE Computer Society
ページ336-343
ページ数8
ISBN(電子版)9789526865324
DOI
出版物ステータス出版済み - 1 8 2018
イベント21st Conference of Open Innovations Association, FRUCT 2017 - Helsinki, フィンランド
継続期間: 11 6 201711 10 2017

その他

その他21st Conference of Open Innovations Association, FRUCT 2017
フィンランド
Helsinki
期間11/6/1711/10/17

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Electrical and Electronic Engineering

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  • これを引用

    Uda, S., & Kubota, H. (2018). Sparse Gaussian graphical model with missing values. : Proceedings of the 21st Conference of Open Innovations Association, FRUCT 2017 (pp. 336-343). IEEE Computer Society. https://doi.org/10.23919/FRUCT.2017.8250201