A personalized network framework reveals predictive axis of anti-TNF response across diseases.

ABSTRACT: Personalized treatment of complex diseases has been mostly predicated on biomarker identification of one drug-disease combination at a time. Here, we use a computational approach termed Disruption Networks to generate a data type, contextualized by cell-centered individual-level networks, that captures biology otherwise overlooked when performing standard statistics. This data type extends beyond the "feature level space", to the "relations space", by quantifying individual-level breaking or rewiring of cross-feature relations. Applying Disruption Networks to dissect high-dimensional blood data, we discover and validate that the RAC1-PAK1 axis is predictive of anti-TNF response in inflammatory bowel disease. Intermediate monocytes, which correlate with the inflammatory state, play a key ro

SUBMITTER: Gerassy-Vainberg S 

PROVIDER: S-EPMC10829759 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature