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Identification of an Efficient Gene Expression Panel for Glioblastoma Classification.
Crisman, Thomas J; Zelaya, Ivette; Laks, Dan R; Zhao, Yining; Kawaguchi, Riki; Gao, Fuying; Kornblum, Harley I; Coppola, Giovanni.
Afiliación
  • Crisman TJ; Semel Institute for Neuroscience & Human Behavior and Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, California 90095, United States of America.
  • Zelaya I; Semel Institute for Neuroscience & Human Behavior and Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, California 90095, United States of America.
  • Laks DR; Department of Biological Chemistry, University of California Los Angeles, Los Angeles, California 90095, United States of America.
  • Zhao Y; Semel Institute for Neuroscience & Human Behavior and Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, California 90095, United States of America.
  • Kawaguchi R; Semel Institute for Neuroscience & Human Behavior and Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, California 90095, United States of America.
  • Gao F; Semel Institute for Neuroscience & Human Behavior and Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, California 90095, United States of America.
  • Kornblum HI; Semel Institute for Neuroscience & Human Behavior and Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, California 90095, United States of America.
  • Coppola G; Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research, University of California Los Angeles, Los Angeles, California 90095, United States of America.
PLoS One ; 11(11): e0164649, 2016.
Article en En | MEDLINE | ID: mdl-27855170
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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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Neoplasias Encefálicas / Glioblastoma / Biología Computacional / Perfilación de la Expresión Génica / Transcriptoma Tipo de estudio: Diagnostic_studies / Prognostic_studies Límite: Humans Idioma: En Revista: PLoS One Asunto de la revista: CIENCIA / MEDICINA Año: 2016 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Neoplasias Encefálicas / Glioblastoma / Biología Computacional / Perfilación de la Expresión Génica / Transcriptoma Tipo de estudio: Diagnostic_studies / Prognostic_studies Límite: Humans Idioma: En Revista: PLoS One Asunto de la revista: CIENCIA / MEDICINA Año: 2016 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos