RegNetAgents: Multi-Agent Framework for Cross-Network Regulatory Driver Identification in Cancer Genomics
RegNetAgents is a multi-agent AI framework designed to identify regulatory driver genes across heterogeneous gene regulatory networks by integrating TCGA and single-cell data. The system uses LangGraph DAG workflows and OncoKB annotations to rank candidate regulators, demonstrating significant enrichment for known cancer genes in breast and colorectal cancer datasets. It also includes modules for evaluating oncogenic potential, druggability, and clinical relevance, supporting end-to-end interpretation from candidate identification to hypothesis generation.
Why it matters: This framework introduces an interpretable AI approach for systematically identifying cancer driver genes across multiple regulatory networks, which could accelerate target discovery and hypothesis generation in cancer genomics.
Full story at: arXiv AI/ML ↗