Identifying network biomarkers of cancer by sample-specific differential network.

ABSTRACT: Abundant datasets generated from various big science projects on diseases have presented great challenges and opportunities, which contributed to unfolding the complexity of diseases. The discovery of disease-associated molecular networks for each individual plays an important role in personalized therapy and precision treatment of cancer-based on the reference networks. However, there are no effective ways to distinguish the consistency of different reference networks. In this study, we developed a statistical method, i.e. a sample-specific differential network (SSDN), to construct and analyze such networks based on gene expression of a single sample against a reference dataset. We proved that the SSDN is structurally consistent even with different reference datasets if the reference data

SUBMITTER: Zhang Y 

PROVIDER: S-EPMC9202129 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature