Network-based integration of multi-omics data for clinical outcome prediction in neuroblastoma.

ABSTRACT: Multi-omics data are increasingly being gathered for investigations of complex diseases such as cancer. However, high dimensionality, small sample size, and heterogeneity of different omics types pose huge challenges to integrated analysis. In this paper, we evaluate two network-based approaches for integration of multi-omics data in an application of clinical outcome prediction of neuroblastoma. We derive Patient Similarity Networks (PSN) as the first step for individual omics data by computing distances among patients from omics features. The fusion of different omics can be investigated in two ways: the network-level fusion is achieved using Similarity Network Fusion algorithm for fusing the PSNs derived for individual omics types; and the feature-level fusion is achieved by fusing the

SUBMITTER: Wang C 

PROVIDER: S-EPMC9475034 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature