Human Glioma Diagnosis from Gene Expression Data View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2003-01-01

AUTHORS

Gregory N. Fuller , Kenneth R. Hess , Cristian Mircean , Ioan Tabus , Ilya Shmulevich , Chang Hun Rhee , Kenneth D. Aldape , Janet M. Bruner , Raymond A. Sawaya , Wei Zhang

ABSTRACT

ConclusionsThe practice of clinical genomics has patient care applications as the ultimate goal. Geneexpression profiling studies have clearly demonstrated the utility of genomics in the areas of molecular classification, identification of novel subgroups(so-called “diseases within disease”), identification of new markers for diagnosis, and identification of novel targets for therapeutic intervention. Genomics data analyses have further demonstrated that an approach based on single gene analysis is not sufficiently robust for diagnostic applications, especially when multiple subtypes of diseases are involved. In contrast, combinations of genes provide much more useful information. Although gene expression profiling projects normally include the analysis of hundreds or thousands of genes in a single experiment, many of the genes thusanalyzed have very little or no disease classification potential. Such genes should be excluded from diagnosis chips, which optimally will contain only a small subset of genes. Focused chips of this type are also more amenable to the strict quality controls and conditioning that are required for clinical applications. If the analysis is extended to the protein level via immunohistochemistry, the number of genes to be tested should similarly not be too large if practical clinical utility is sought. In this paper, we have described a methodology for selecting a subset of genes that in combination have the greatest discriminating power for the four major subtypes of human gliomas. The 40 genes thus identified may potentially constitute a clinically useful diagnostic chip. More... »

PAGES

241-256

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/0-306-47825-0_14

DOI

http://dx.doi.org/10.1007/0-306-47825-0_14

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1013995771


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