Random forest-based imputation outperforms other methods for imputing LC-MS metabolomics data: a comparative study
Abstract
LC-MS technology makes it possible to measure the relative abundance of numerous molecular features of a sample in single analysis. However, especially non-targeted metabolite profiling approaches generate vast arrays of data that are prone to aberrations such as missing values. No matter the reason for the missing values in the data, coherent and complete data matrix is always a pre-requisite for accurate and reliable statistical analysis....
Paper Details
Title
Random forest-based imputation outperforms other methods for imputing LC-MS metabolomics data: a comparative study
Published Date
Oct 11, 2019
Journal
Volume
20
Issue
1
Pages
492 - 492
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