AutoFocus: a hierarchical framework to explore multi-omic disease associations spanning multiple scales of biomolecular interaction.

ABSTRACT: Recent advances in high-throughput measurement technologies have enabled the analysis of molecular perturbations associated with disease phenotypes at the multi-omic level. Such perturbations can range in scale from fluctuations of individual molecules to entire biological pathways. Data-driven clustering algorithms have long been used to group interactions into interpretable functional modules; however, these modules are typically constrained to a fixed size or statistical cutoff. Furthermore, modules are often analyzed independently of their broader biological context. Consequently, such clustering approaches limit the ability to explore functional module associations with disease phenotypes across multiple scales. Here, we introduce AutoFocus, a data-driven method that hierarchically or

SUBMITTER: Schweickart A 

PROVIDER: S-EPMC11377741 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature