TIGER: technical variation elimination for metabolomics data using ensemble learning architecture
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DOI: 10.1093/bib/bbab535
Abstract: Abstract Large metabolomics datasets inevitably contain unwanted technical variations which can obscure meaningful biological signals and affect how this information is applied to personalized healthcare. Many methods have been developed to handle unwanted variations. However,… read more here.
Keywords: ensemble learning; metabolomics data; learning architecture; tiger technical ... See more keywords