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Identification of an Efficient Gene Expression Panel for Glioblastoma Classification

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

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.

Suggested Citation

  • Thomas J Crisman & Ivette Zelaya & Dan R Laks & Yining Zhao & Riki Kawaguchi & Fuying Gao & Harley I Kornblum & Giovanni Coppola, 2016. "Identification of an Efficient Gene Expression Panel for Glioblastoma Classification," PLOS ONE, Public Library of Science, vol. 11(11), pages 1-19, November.
    • Handle: RePEc:plo:pone00:0164649
    DOI: 10.1371/journal.pone.0164649
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    1. Ilya Kupershmidt & Qiaojuan Jane Su & Anoop Grewal & Suman Sundaresh & Inbal Halperin & James Flynn & Mamatha Shekar & Helen Wang & Jenny Park & Wenwu Cui & Gregory D Wall & Robert Wisotzkey & Satnam , 2010. "Ontology-Based Meta-Analysis of Global Collections of High-Throughput Public Data," PLOS ONE, Public Library of Science, vol. 5(9), pages 1-13, September.
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