Sparse Gaussian graphical model with missing values
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...
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doaj-4997c7ae11c44228bdf5be1687b188612020-11-24T22:16:31ZengFRUCTProceedings of the XXth Conference of Open Innovations Association FRUCT2305-72542343-07372017-11-015622133634310.23919/FRUCT.2017.8250201Sparse Gaussian graphical model with missing valuesShinsuke Uda0Hiroyuki Kubota1Medical Institute of Bioregulation, Kyushu University, Fukuoka, JapanMedical Institute of Bioregulation, Kyushu University, Fukuoka, JapanRecent 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.https://fruct.org/publications/fruct21/files/Uda.pdf BioinformaticsInformation scienceMachine learning |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Shinsuke Uda Hiroyuki Kubota |
spellingShingle |
Shinsuke Uda Hiroyuki Kubota Sparse Gaussian graphical model with missing values Proceedings of the XXth Conference of Open Innovations Association FRUCT Bioinformatics Information science Machine learning |
author_facet |
Shinsuke Uda Hiroyuki Kubota |
author_sort |
Shinsuke Uda |
title |
Sparse Gaussian graphical model with missing values |
title_short |
Sparse Gaussian graphical model with missing values |
title_full |
Sparse Gaussian graphical model with missing values |
title_fullStr |
Sparse Gaussian graphical model with missing values |
title_full_unstemmed |
Sparse Gaussian graphical model with missing values |
title_sort |
sparse gaussian graphical model with missing values |
publisher |
FRUCT |
series |
Proceedings of the XXth Conference of Open Innovations Association FRUCT |
issn |
2305-7254 2343-0737 |
publishDate |
2017-11-01 |
description |
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. |
topic |
Bioinformatics Information science Machine learning |
url |
https://fruct.org/publications/fruct21/files/Uda.pdf
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work_keys_str_mv |
AT shinsukeuda sparsegaussiangraphicalmodelwithmissingvalues AT hiroyukikubota sparsegaussiangraphicalmodelwithmissingvalues |
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1725789315429040128 |