AMEND 2.0: module identification and multi-omic data integration with multiplex-heterogeneous graphs.

ABSTRACT:

Background

Multi-omic studies provide comprehensive insight into biological systems by evaluating cellular changes between normal and pathological conditions at multiple levels of measurement. Biological networks, which represent interactions or associations between biomolecules, have been highly effective in facilitating omic analysis. However, current network-based methods lack generalizability to accommodate multiple data types across a range of diverse experiments.

Results

We present AMEND 2.0, an updated active module identification method which can analyze multiplex and/or heterogeneous networks integrated with multi-omic data in a highly generalizable framework, in contrast to existing methods, which are mostly appropriate for at most two specific omic types. It is po

SUBMITTER: Boyd SS 

PROVIDER: S-EPMC11800622 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature