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.
Type de document :
Article dans une revue
Oncologist, AlphaMed Press, 2018, 〈10.1634/theoncologist.2017-0495〉
Liste complète des métadonnées

https://hal-amu.archives-ouvertes.fr/hal-01858235
Contributeur : Dominique Figarella-Branger <>
Soumis le : lundi 20 août 2018 - 11:47:33
Dernière modification le : vendredi 18 janvier 2019 - 01:06:00

Identifiants

Citation

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, AlphaMed Press, 2018, 〈10.1634/theoncologist.2017-0495〉. 〈hal-01858235〉

Partager

Métriques

Consultations de la notice

126