Identification of an Efficient Gene Expression Panel for Glioblastoma Classification

Thomas J Crisman, Ivette Zelaya, Dan R Laks, Yining Zhao, Riki Kawaguchi, Fuying Gao, Harley I Kornblum and Giovanni Coppola

PLOS ONE, 2016, vol. 11, issue 11, 1-19

Abstract: 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 http://simplegbm.semel.ucla.edu.

Date: 2016
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0164649

DOI: 10.1371/journal.pone.0164649

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