Predicting clinical outcome of neuroblastoma patients using an integrative network-based approach.

ABSTRACT:

Background

One of the main current challenges in computational biology is to make sense of the huge amounts of multidimensional experimental data that are being produced. For instance, large cohorts of patients are often screened using different high-throughput technologies, effectively producing multiple patient-specific molecular profiles for hundreds or thousands of patients.

Results

We propose and implement a network-based method that integrates such patient omics data into Patient Similarity Networks. Topological features derived from these networks were then used to predict relevant clinical features. As part of the 2017 CAMDA challenge, we have successfully applied this strategy to a neuroblastoma dataset, consisting of genomic and transcriptomic data. In particular,

SUBMITTER: Tranchevent LC 

PROVIDER: S-EPMC5992838 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature