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About Gromski Et Al. - 2015 - A Tutorial Review Metabolomics and Partial Least Squares-Discriminant Analysis - A Marriage of Convenience or A
This document compares different statistical analysis techniques used in metabolomics studies, including partial least squares-discriminant analysis (PLS-DA), principal component-discriminant function analysis (PC-DFA), support vector machines (SVM), and random forests (RF). The document highlights the pros and cons of each technique and suggests they could be used to augment PLS-DA or as alternatives for analyzing large, complex metabolomics datasets, as they may provide more robust models through feature selection and data reduction.
Detailed Information
| Author: | ['yannick brunato'] |
|---|---|
| Publication Year: | 2022 |
| Pages: | 14 |
| Language: | English |
| Format: | |
| Price: | FREE |
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