NetMIM: network-based multi-omics integration with block missingness for biomarker selection and disease outcome prediction.

ABSTRACT: Compared with analyzing omics data from a single platform, an integrative analysis of multi-omics data provides a more comprehensive understanding of the regulatory relationships among biological features associated with complex diseases. However, most existing frameworks for integrative analysis overlook two crucial aspects of multi-omics data. Firstly, they neglect the known dependencies among biological features that exist in highly credible biological databases. Secondly, most existing integrative frameworks just simply remove the subjects without full omics data to handle block missingness, resulting in decreasing statistical power. To overcome these issues, we propose a network-based integrative Bayesian framework for biomarker selection and disease outcome prediction based on multi-

SUBMITTER: Zhu B 

PROVIDER: S-EPMC11407451 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature