Analysis of metabolite profile data using batch-learning self-organizing maps

Novel tools are needed for efficient analysis and visualization of the massive data sets associated with metabolomics. Here, we describe a batch-learning self-organizing map (BL-SOM) for metabolome informatics that makes the learning process and resulting map independent of the order of data input....

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Bibliographic Details
Published in:Journal of plant biology = Singmul Hakhoe chi 2007, 50(4), , pp.517-521
Main Authors: Kim, Jae Kwang, Cho, Myoung Rae, Baek, Hyung Jin, Ryu, Tae Hun, Yu, Chang Yeon, Kim, Myong Jo, Fukusaki, Eiichiro, Kobayashi, Akio
Format: Article
Language:English
Subjects:
Amino acids
Clustering
Glycolysis
Informatics
Learning
Massive data points
Metabolites
Metabolomics
Self organizing maps
생물학
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Summary:Novel tools are needed for efficient analysis and visualization of the massive data sets associated with metabolomics. Here, we describe a batch-learning self-organizing map (BL-SOM) for metabolome informatics that makes the learning process and resulting map independent of the order of data input. This approach was successfully used in analyzing and organizing the metabolome data forArabidopsis thaliana cells cultured under salt stress. Our 6 × 4 matrix presented patterns of metabolite levels at different time periods. A negative correlation was found between the levels of amino acids and metabolites related to glycolysis metabolism in response to this stress. Therefore, BL-SOM could be an excellent tool for clustering and visualizing high dimensional, complex metabolome data in a single map.
ISSN:1226-9239
1867-0725
DOI:10.1007/BF03030693