title: Identification of an Efficient Gene Expression Panel for Glioblastoma Classification.
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.
authors.affiliation: 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.
authors.forename: Thomas J
authors.identifier: http://orcid.org/0000-0001-6235-8110
authors.initials: TJ
authors.lastname: Crisman
authors.affiliation: 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.
authors.forename: Ivette
authors.initials: I
authors.lastname: Zelaya
authors.affiliation: Department of Biological Chemistry, University of California Los Angeles, Los Angeles, California 90095, United States of America.
authors.forename: Dan R
authors.initials: DR
authors.lastname: Laks
authors.affiliation: 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.
authors.forename: Yining
authors.initials: Y
authors.lastname: Zhao
authors.affiliation: 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.
authors.forename: Riki
authors.initials: R
authors.lastname: Kawaguchi
authors.affiliation: 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.
authors.forename: Fuying
authors.initials: F
authors.lastname: Gao
authors.affiliation: 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.|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.|Jonsson Comprehensive Cancer Center, University of California Los Angeles, Los Angeles, California 90095, United States of America.
authors.forename: Harley I
authors.initials: HI
authors.lastname: Kornblum
authors.affiliation: 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.
authors.forename: Giovanni
authors.initials: G
authors.lastname: Coppola
mesh_terms: D000465:Algorithms; D001932:Brain Neoplasms / Q000235:genetics* / Q000401:mortality; D019295:Computational Biology / Q000379:methods*; D066264:Datasets as Topic; D020869:Gene Expression Profiling / Q000379:methods; D023281:Genomics / Q000379:methods; D005909:Glioblastoma / Q000235:genetics* / Q000401:mortality; D006801:Humans; D053208:Kaplan-Meier Estimate; D058977:Molecular Sequence Annotation; D011379:Prognosis; D015203:Reproducibility of Results; D059467:Transcriptome; D064878:Web Browser