RegNetAgents：一种用于癌症基因组学中跨网络调控驱动因子识别的多智能体框架
RegNetAgents: A Multi-Agent Framework for Cross-Network Regulatory Driver Identification in Cancer Genomics
浏览论文内容
中文总结 AI 辅助
RegNetAgents是用于癌症基因组学跨网络调控驱动因子识别的多智能体框架，整合不同网络，经双网络分类等操作，对候选因子按证据一致性排名，在乳腺癌和结直肠癌焦点基因中识别出显著富集的调控因子，建立可解释AI框架。
中文摘要 AI 辅助
我们介绍了RegNetAgents，这是一个面向人工智能的多智能体框架，用于在异构基因调控网络中进行结构化、查询驱动的调控候选因子识别。该系统通过将TCGA衍生的癌症网络与GREmLN项目的大规模单细胞调控网络相结合，实现了对批量肿瘤和单细胞衍生的ARACNe网络的统一分析。对于给定的焦点基因，该框架进行双网络分类、使用OncoKB注释进行癌症基因过滤以及对肿瘤衍生的调控关系进行作用模式（MoA）分配。候选因子通过跨网络（两者、仅TCGA、仅GREmLN）的证据一致性进行排名。该系统作为多智能体LangGraph DAG工作流程实现，可通过统一的Python API和模型上下文协议（MCP）客户端访问，作为预计算调控网络上的下游分析层，而不是网络推理方法。在11个乳腺癌（BRCA）和12个结直肠癌（COAD）焦点基因中，RegNetAgents识别出的候选调控因子在OncoKB注释的癌症基因中显著富集。TCGA衍生的候选因子显示出强烈的富集（BRCA的Stouffer Z = 6.69，COAD的Stouffer Z = 6.95），而GREmLN衍生的候选因子也显示出显著的富集（BRCA的Z = 5.51，COAD的Z = 7.06；所有p < 0.0001）。在内源基因或非驱动控制基因集中未观察到富集，支持信号特异性。一个扩展模块能够对致癌潜力、可药物性、临床相关性和网络脆弱性进行结构化评估，支持从候选因子识别到生物学假设生成的端到端解释。RegNetAgents为癌症基因组学中的跨网络调控候选因子识别建立了一个可解释的人工智能框架。
英文摘要
We introduce RegNetAgents, an AI-oriented multi-agent framework for structured, query-driven regulatory candidate identification across heterogeneous gene regulatory networks. It integrates bulk tumor (TCGA) and single-cell (GREmLN project) ARACNe networks and labels each candidate regulator by the network or networks in which it appears (Both, TCGA-only, GREmLN-only). For a given focal gene, the framework finds its regulators in both networks and labels each by source, flags those that are known cancer driver genes (IntOGen), and, for tumor-network regulators, gives the mode of action (MoA; activating or repressive). It is implemented as a multi-agent LangGraph state-graph workflow, accessible through a Python API and a Model Context Protocol (MCP) client, and operates as a downstream analytical layer over precomputed networks rather than a network inference method. For example, a single query for CTNNB1 in BRCA returns two tumor-specific (TCGA-only) driver-gene regulators, DDR2 and IL6ST, both with activating MoA. Across twelve breast cancer (BRCA) and thirteen colorectal cancer (COAD) driver genes, we compared each gene's tumor-network-only (TCGA-only) regulators with the TCGA-only regulators of random non-driver genes. Regulators as a group are about 2.7-fold richer in driver genes than genes overall, so almost any gene's regulators look enriched when tested against all genes. We therefore used random genes as the baseline. In COAD, the cancer genes' regulators include modestly but consistently more IntOGen driver genes than random genes' regulators do (nominal p = 0.012-0.036 across five random samples); in BRCA the difference is borderline (p = 0.048-0.083). Housekeeping and non-driver control genes show no such excess in the tumor-network tier, and the same comparison for GREmLN-only regulators shows none (p >= 0.20). Code: https://github.com/jab57/RegNetAgents
发表机构
- Bird AI Solutions（Bird人工智能解决方案公司）
机构由 AI 辅助整理，请以论文原文为准。
