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Example random walk. Gene Set Enrichment Analysis random walk for a flow <t>cytometry</t> set comprised of 27 variables
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Example random walk. Gene Set Enrichment Analysis random walk for a flow cytometry set comprised of 27 variables

Journal: BMC Bioinformatics

Article Title: A methodology for exploring biomarker – phenotype associations: application to flow cytometry data and systemic sclerosis clinical manifestations

doi: 10.1186/s12859-015-0722-x

Figure Lengend Snippet: Example random walk. Gene Set Enrichment Analysis random walk for a flow cytometry set comprised of 27 variables

Article Snippet: The methods utilized in the work involve: (1) clinical and peripheral blood flow cytometry data ( I mmune R esponse I n S cleroderma, IRIS) from consented patients followed at the Johns Hopkins Scleroderma Center. (2) machine learning (Conditional Random Forests - CRF) coupled with Gene Set Enrichment Analysis (GSEA) to identify subsets of FC variables that are highly effective in classifying ILD patients; and (3) stochastic simulation to design, train and validate ILD risk screening tools.

Techniques: Flow Cytometry