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Extra resources for [Journal] Pediatrics Clinics of North America. Volume 53. Number 6

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Interpreting the biological meaning of gene expression experiments can be challenging; however, numerous tools have been developed to assist investigators. Clustering [25], which groups genes on the basis of similar expression patterns with the assumption that they are co-coordinately regulated or possibly part of the same signaling pathway, was one of the first tools developed and widely used to interpret microarray data [25]. Other widely used, related methods to discover patterns of gene expression common to a particular physiological state include supervised clustering, principle component analysis, self-organizing maps, and linear discriminant analysis.

To test whether this 50-gene predictor set could classify tumor class accurately, they attempted to classify 34 independent tumor samples. The 50gene expression signature could classify 29 of the 34 samples with 100% 584 HESSNER et al accuracy. This study was a true proof of principle that microarray analysis might be sufficiently accurate to classify tumor types as a clinical diagnostic tool. Many studies subsequently have followed and determined signatures of different ALL subclasses [32–34], subtypes that respond differently to various treatments [29,35,36].

Juvenile rheumatoid arthritis: outcome and treatment for the 1990s. Rheum Dis Clin North Am 1991;17(4):891–905. [47] de Benedetti F, Massa M, Robbioni P, et al. Correlation of serum interleukin-6 levels with joint involvement and thrombocytosis in systemic juvenile rheumatoid arthritis. Arthritis Rheum 1991;34(9):1158–63. [48] Pascual V, Allantaz F, Arce E, et al. Role of interleukin-1 (IL-1) in the pathogenesis of systemic onset juvenile idiopathic arthritis and clinical response to IL-1 blockade.

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[Journal] Pediatrics Clinics of North America. Volume 53. Number 6

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