Identification of an Efficient Gene Expression Panel for Glioblastoma Classification.
In: PLoS ONE, Jg. 11 (2016-11-17), Heft 11, S. 1-19
Online
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Zugriff:
We present here a novel genetic algorithm-based random forest (GARF) modeling technique that enables a reduction in the complexity of large gene disease signatures to highly accurate, greatly simplified gene panels. When applied to 803 glioblastoma multiforme samples, this method allowed the 840-gene Verhaak et al. gene panel (the standard in the field) to be reduced to a 48-gene classifier, while retaining 90.91% classification accuracy, and outperforming the best available alternative methods. Additionally, using this approach we produced a 32-gene panel which allows for better consistency between RNA-seq and microarray-based classifications, improving cross-platform classification retention from 69.67% to 86.07%. A webpage producing these classifications is available at . [ABSTRACT FROM AUTHOR]
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Titel: |
Identification of an Efficient Gene Expression Panel for Glioblastoma Classification.
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Autor/in / Beteiligte Person: | Crisman, Thomas J. ; Zelaya, Ivette ; Laks, Dan R. ; Zhao, Yining ; Kawaguchi, Riki ; Gao, Fuying ; Kornblum, Harley I. ; Coppola, Giovanni |
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Zeitschrift: | PLoS ONE, Jg. 11 (2016-11-17), Heft 11, S. 1-19 |
Veröffentlichung: | 2016 |
Medientyp: | academicJournal |
ISSN: | 1932-6203 (print) |
DOI: | 10.1371/journal.pone.0164649 |
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