Predicting clinical outcomes in neuroblastoma with genomic data integration


Baali I., Acar D. A. E. , Aderinwale T. W. , HafezQorani S., Kazan H.

BIOLOGY DIRECT, vol.13, 2018 (Journal Indexed in SCI) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 13
  • Publication Date: 2018
  • Doi Number: 10.1186/s13062-018-0223-8
  • Title of Journal : BIOLOGY DIRECT
  • Keywords: Neuroblastoma, Data integration, Cancer subtypes, Kernel k-means, EXPRESSION-BASED CLASSIFICATION, CLUSTER VALIDATION

Abstract

Background: Neuroblastoma is a heterogeneous disease with diverse clinical outcomes. Current risk group models require improvement as patients within the same risk group can still show variable prognosis. Recently collected genome-wide datasets provide opportunities to infer neuroblastoma subtypes in a more unified way. Within this context, data integration is critical as different molecular characteristics can contain complementary signals. To this end, we utilized the genomic datasets available for the SEQC cohort patients to develop supervised and unsupervised models that can predict disease prognosis.