知识图谱的技术验证
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一，biocypher
https://biocypher.org/
https://github.com/earlham-sherlock/earlham-sherlock.github.io
https://www.nature.com/articles/s41587-023-01848-y
41587_2023_1848_MOESM1_ESM.pdf
biocypher认为建立特定任务KG的能力很重要，因为直接标准化生物医学知识的表示不适合社区中的各种研究任务。需要一个框架能快速为领域构建不同的KG。
a, Threefold modularity: resources (left) and ontologies (bottom left) are combined to yield a knowledge graph (right). The mapping of entities to ontology concepts is realized by shareable configuration, which can be iteratively optimized.
不同的资源和本体组合成知识库。实体与本体之间的映射可以不断地使用共享配置进行实现。b, Initially, we transform commonly used, curated ‘secondary’ resources into configurable, task-specific knowledge graphs in various output formats. Incrementally, these secondary adapters will be replaced by primary resource adapters (Supplementary Fig. 1). Colored panels in a,b indicate parts of the BioCypher ecosystem.
c, Agreeing on a common representational framework allows harmonization of task-specific data sources to answer complex queries across biomedical domains. For instance, starting at mass spectrometry measurements of a patient’s tumor (left), one could go through clinical annotations to genetic dependencies from the Dependency Map project to identify potential drug targets or through pathway or process annotations in Reactome and IntAct, identify relevant ligand–receptor pairs using OmniPath, and use CROssBAR to perform drug discovery or repurposing for these receptors. Panels correspond to resources; although we work on most of the displayed resources, the figure is used for illustrative purposes and does not depict an existing pipeline.
安装
biocypher使用了poetry管理依赖 poetry需要运行在一个虚拟环境中 biocypher依赖python3.9 sudo apt install python3.9-venv mkdir biocypher && cd "$_" python3.9 -m venv .biocypher source .biocypher/bin/activate pip install -U pip setuptools pip install poetry -
二，知识图谱和LLM的结合
Unifying Large Language Models and Knowledge Graphs: A Roadmap
2306.08302.pdf -
三，BioDWH2: An Automated Graph-Based Data Warehouse and Mapping Tool
https://biodwh2.github.io/
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