Identification of an Efficient Gene Expression Panel for Glioblastoma Classification

نویسندگان

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

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.

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عنوان ژورنال:

دوره 11  شماره 

صفحات  -

تاریخ انتشار 2016