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Bioinformatics Tools for Cancer Metabolomics
Abstract
It is well known that significant metabolic change take place as cells are transformed from normal to malignant. The article begins by describing different metabolomics technologies and data generation techniques. Overview of the data pre-processing techniques is provided and multivariate data analysis techniques are discussed and illustrated with case studies, including principal component analysis, clustering techniques, self-organizing maps, partial least squares, and discriminant function analysis. Also included is a discussion of available software packages.
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