TY - GEN
T1 - Sparse Gaussian graphical model with missing values
AU - Uda, Shinsuke
AU - Kubota, Hiroyuki
N1 - Funding Information:
The authors would like to thank Ms. Y. Yamauchi for her assistance in compiling the references. The present study was supported by a Grant-in-Aid for Scientific Research on Innovative Areas (Grant Number 16H01551) from the Japan Society for the Promotion of Science (JSPS) .
Publisher Copyright:
© 2017 FRUCT.
PY - 2017/7/1
Y1 - 2017/7/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85045153300&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85045153300&partnerID=8YFLogxK
U2 - 10.23919/FRUCT.2017.8250201
DO - 10.23919/FRUCT.2017.8250201
M3 - Conference contribution
AN - SCOPUS:85045153300
T3 - Conference of Open Innovation Association, FRUCT
SP - 336
EP - 343
BT - Proceedings of the 21st Conference of Open Innovations Association, FRUCT 2017
PB - IEEE Computer Society
T2 - 21st Conference of Open Innovations Association, FRUCT 2017
Y2 - 6 November 2017 through 10 November 2017
ER -