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- W2104653669 abstract "Mass-spectra based proteomic profiles have received widespread attention as potential tools for biomarker discovery and early disease diagnosis. A major data-analytical problem involved is the extremely high dimensionality (i.e. number of features or variables) of proteomic data, in particular when the sample size is small. This article reviews dimensionality reduction methods that have been used in proteomic biomarker studies. It then focuses on the problem of selecting the most appropriate method for a specific task or dataset, and proposes method combination as a potential alternative to single-method selection. Finally, it points out the potential of novel dimension reduction techniques, in particular those that incorporate domain knowledge through the use of informative priors or causal inference." @default.
- W2104653669 created "2016-06-24" @default.
- W2104653669 creator A5034196019 @default.
- W2104653669 creator A5084831490 @default.
- W2104653669 date "2007-09-28" @default.
- W2104653669 modified "2023-10-14" @default.
- W2104653669 title "Approaches to dimensionality reduction in proteomic biomarker studies" @default.
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- W2104653669 doi "https://doi.org/10.1093/bib/bbn005" @default.
- W2104653669 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/18310106" @default.
- W2104653669 hasPublicationYear "2007" @default.
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