Dynamic network curvature analysis of gene expression reveals novel potential therapeutic targets in sarcoma.

ABSTRACT: Network properties account for the complex relationship between genes, making it easier to identify complex patterns in their interactions. In this work, we leveraged these network properties for dual purposes. First, we clustered pediatric sarcoma tumors using network information flow as a similarity metric, computed by the Wasserstein distance. We demonstrate that this approach yields the best concordance with histological subtypes, validated against three state-of-the-art methods. Second, to identify molecular targets that would be missed by more conventional methods of analysis, we applied a novel unsupervised method to cluster gene interactomes represented as networks in pediatric sarcoma. RNA-Seq data were mapped to protein-level interactomes to construct weighted networks that were

SUBMITTER: Elkin R 

PROVIDER: S-EPMC10766622 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature