Active learning-guided mechanistic modeling of CXCL9 regulation in pancreatic cancer

ABSTRACT: Cold tumors like pancreatic cancer suffer from poor immune infiltration, limiting effective anti-tumor responses. The chemokine CXCL9 promotes immune cell recruitment, but the signaling mechanisms regulating its expression in tumor cells remain poorly understood and underexplored as targets for modulation. We present a framework that integrates active learning with mechanistic logic-ODE models to guide perturbation screenings and uncover regulators of CXCL9 in pancreatic cancer cells. Using perturbation-response data and curated prior knowledge, we trained interpretable models to identify signaling mechanisms that enhance CXCL9 expression and prioritize drug combinations. Active learning enabled data-efficient model refinement and guided informative experiments under resource constraints.

SUBMITTER: Bi-rong Wang 

PROVIDER: S-SCDT-10_1038-S44320-026-00221-W | biostudies-other |

REPOSITORIES: biostudies-other