Optimizing differential expression analysis for proteomics data via high-performing rules and ensemb...
Optimizing differential expression analysis for proteomics data via high-performing rules and ensemble inference
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London: Nature Publishing Group UK
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English
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London: Nature Publishing Group UK
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Identification of differentially expressed proteins in a proteomics workflow typically encompasses five key steps: raw data quantification, expression matrix construction, matrix normalization, missing value imputation (MVI), and differential expression analysis. The plethora of options in each step makes it challenging to identify optimal workflow...
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Optimizing differential expression analysis for proteomics data via high-performing rules and ensemble inference
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TN_cdi_doaj_primary_oai_doaj_org_article_0ddb2d466c9f4855943315c720a89d99
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https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_0ddb2d466c9f4855943315c720a89d99
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ISSN
2041-1723
E-ISSN
2041-1723
DOI
10.1038/s41467-024-47899-w
