Machine Learning for Better Prognostic Stratification and Driver Gene Identification Using Somatic Copy Number Variations in Anaplastic Oligodendroglioma - Aix-Marseille Université Access content directly
Journal Articles Oncologist Year : 2018

Machine Learning for Better Prognostic Stratification and Driver Gene Identification Using Somatic Copy Number Variations in Anaplastic Oligodendroglioma

Abstract

1p/19q-codeleted anaplastic gliomas have variable clinical behavior. We have recently shown that the common 9p21.3 allelic loss is an independent prognostic factor in this tumor type. The aim of this study is to identify less frequent genomic copy number variations (CNVs) with clinical importance that may shed light on molecular oncogenesis of this tumor type.

Dates and versions

hal-01858235 , version 1 (20-08-2018)

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Shai Rosenberg, Francois Ducray, Agusti Alentorn, Caroline Dehais, Nabila Elarouci, et al.. Machine Learning for Better Prognostic Stratification and Driver Gene Identification Using Somatic Copy Number Variations in Anaplastic Oligodendroglioma. Oncologist, 2018, ⟨10.1634/theoncologist.2017-0495⟩. ⟨hal-01858235⟩
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