用大模型整合神经科学、基因与疾病知识图谱，构建跨层次认知研究框架。
MultiCNKG: Integrating Cognitive Neuroscience, Gene, and Disease Knowledge Graphs Using Large Language Models
- 基于GPT-4实现三类知识图谱的实体对齐与语义融合
- 生成含6.9K节点、11.3K边的统一图谱，覆盖基因到行为多层级关系
- 适用于个性化医疗与认知障碍诊断，助力神经科学研究
大型语言模型（LLMs）的兴起推动了生物医学与认知科学中知识图谱（KG）的整合，克服了传统机器学习在捕捉基因、疾病与认知过程间复杂语义关联方面的局限。本文提出MultiCNKG框架，融合认知神经科学知识图谱（CNKG，2.9K节点、4.3K边，9种节点类型、20种边类型）、基因本体（GO，43K节点、75K边，3种节点类型、4种边类型）和疾病本体（DO，11.2K节点、8.8K边，1种节点类型、2种边类型）。利用GPT-4进行实体对齐、语义相似性计算与图谱增强，构建出涵盖6.9K节点（5类，如基因、疾病、认知过程）和11.3K条边（7类，如导致、相关于、调节）的统一知识图谱，实现从分子到行为层面的多层整合。通过精确率（85.20%）、召回率（87.30%）、覆盖率（92.18%）、图一致性（82.50%）、新颖性检测（40.28%）及专家验证（89.50%）评估，证明其稳健性与一致性。链接预测实验显示，TransE（MR: 391, MRR: 0.411）与RotatE（MR: 263, MRR: 0.395）表现优于基准模型如FB15k-237和WN18RR。该图谱可支持个性化医疗、认知障碍诊断及认知神经科学假说生成。
原文摘要 · Abstract (English)
The advent of large language models (LLMs) has revolutionized the integration of knowledge graphs (KGs) in biomedical and cognitive sciences, overcoming limitations in traditional machine learning methods for capturing intricate semantic links among genes, diseases, and cognitive processes. We introduce MultiCNKG, an innovative framework that merges three key knowledge sources: the Cognitive Neuroscience Knowledge Graph (CNKG) with 2.9K nodes and 4.3K edges across 9 node types and 20 edge types; Gene Ontology (GO) featuring 43K nodes and 75K edges in 3 node types and 4 edge types; and Disease Ontology (DO) comprising 11.2K nodes and 8.8K edges with 1 node type and 2 edge types. Leveraging LLMs like GPT-4, we conduct entity alignment, semantic similarity computation, and graph augmentation to create a cohesive KG that interconnects genetic mechanisms, neurological disorders, and cognitive functions. The resulting MultiCNKG encompasses 6.9K nodes across 5 types (e.g., Genes, Diseases, Cognitive Processes) and 11.3K edges spanning 7 types (e.g., Causes, Associated with, Regulates), facilitating a multi-layered view from molecular to behavioral domains. Assessments using metrics such as precision (85.20%), recall (87.30%), coverage (92.18%), graph consistency (82.50%), novelty detection (40.28%), and expert validation (89.50%) affirm its robustness and coherence. Link prediction evaluations with models like TransE (MR: 391, MRR: 0.411) and RotatE (MR: 263, MRR: 0.395) show competitive performance against benchmarks like FB15k-237 and WN18RR. This KG advances applications in personalized medicine, cognitive disorder diagnostics, and hypothesis formulation in cognitive neuroscience.
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