TY - GEN
T1 - Reconstruction of metabolic association networks using high-throughput mass spectrometry data
AU - Koo, Imhoi
AU - Zhang, Xiang
AU - Kim, Seongho
N1 - Funding Information:
This work was also partially supported by National Institute of Health (NIH) grant 1RO1GM087735 through the National Institute of General Medical Sciences (NIGMS).
PY - 2012
Y1 - 2012
N2 - Graphical Gaussian model (GGM) has been widely used in genomics and proteomics to infer biological association networks, but the relative performances of various GGM-based methods are still unclear in metabolomics. The association between two nodes of GGM is calculated by partial correlation as a measure of conditional independence. To estimate the partial correlations with small sample size and large variables, two approaches have been introduced, which are arithmetic mean-based and geometric mean-based methods. In this study, we investigated the effects of these two approaches on constructing association metabolite networks and then compared their performances using partial least squares regression and principal component regression along with shrinkage covariance estimate as a reference. These approaches then are applied to simulated data and real metabolomics data.
AB - Graphical Gaussian model (GGM) has been widely used in genomics and proteomics to infer biological association networks, but the relative performances of various GGM-based methods are still unclear in metabolomics. The association between two nodes of GGM is calculated by partial correlation as a measure of conditional independence. To estimate the partial correlations with small sample size and large variables, two approaches have been introduced, which are arithmetic mean-based and geometric mean-based methods. In this study, we investigated the effects of these two approaches on constructing association metabolite networks and then compared their performances using partial least squares regression and principal component regression along with shrinkage covariance estimate as a reference. These approaches then are applied to simulated data and real metabolomics data.
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U2 - 10.1007/978-3-642-31588-6_21
DO - 10.1007/978-3-642-31588-6_21
M3 - Conference contribution
AN - SCOPUS:84865279284
SN - 9783642315879
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 160
EP - 167
BT - Intelligent Computing Technology - 8th International Conference, ICIC 2012, Proceedings
T2 - 8th International Conference on Intelligent Computing Technology, ICIC 2012
Y2 - 25 July 2012 through 29 July 2012
ER -