Model organism neural circuit knowledge graph

项目摘要 为了从与神经电路相关的大量但不同和孤立的数据中提取更大的价值，我们将利用 本体论、生物信息学和基因组资源联盟的管理，以衍生出人工的 智能(AI)就绪知识图谱。使用人工智能研究神经的计算神经学家的参与 电路分析将有助于指定知识图的形式，演示图的用途，扩展 这些图表从对秀丽隐杆线虫明确定义的神经系统的关注到类似的良好- 定义小鼠视网膜，并与开始将人工智能应用于神经的神经科学研究人员联系 电路。已知实体(例如神经元、小分子和神经肽)和本体论(解剖学， 关系和实验证据)提供了图表的基本数据。经过管理的断言提供了 知识，例如神经元之间的突触或功能连接，神经肽有特定的受体，或 神经肽在特定的神经元中表达)。在这个图模型中，实体是节点，本体论 关系就是边缘。这些提供了有证据支持的推断知识图谱 断言。这种类型的知识图可以应用于基于表型的生物途径 观察包括表情、神经元活动和生物行为，而不是身体相互作用 或酶活性，如用来描述生化途径的那些。要完成生成 知识图谱，我们将提炼相关词汇，重点关注神经回路研究中使用的关系， 我们将调整现有的基础架构，以处理适当的本体、数据模型和管理工具 神经回路数据，我们将通过安排与可计算相结合的简短评论来激励专家贡献 断言。知识图谱将由当地人工智能专家使用，在互联网上发布，并由一名 黑客马拉松。

Project Summary To extract greater value from extensive but disparate and siloed data relevant to neural circuits, we will leverage the ontologies, bioinformatics, and curation of the Alliance of Genome Resources to derive an artificial intelligence (AI) ready knowledge graph. Participation of a computational neuroscientist who uses AI for neural circuit analyses will help specify the form of the knowledge graph, demonstrate the utility of the graphs, extend the graphs from a focus on the well-defined nervous system of Caenorhabditis elegans to the similarly well- defined mouse retina, and connect with neuroscience researchers who are starting to applying AI to neural circuits. Known entities (such as neurons, small molecules, and neuropeptides) and Ontologies (anatomy, relations, and experimental evidence) provide the underlying data of the graph. Curated assertions provide the knowledge, e.g., synaptic or functional connections between neurons, neuropeptide has a specific receptor, or a neuropeptide is expressed in a specific neuron). In this graph model, entities are the nodes, ontological relationships are the edges. These provide an inferred knowledge graph supported by evidence backed assertions. This type of knowledge graph can be applied to biological pathways that are based on phenotype observations including expression, neuronal activity and organismal behavior rather than physical interactions or enzymatic activities such as those used to describe biochemical pathways. To accomplish generation of knowledge graphs, we will refine the relevant vocabularies to focus on relations used in neural circuit research, we will adjust existing infrastructure to handle the appropriate ontologies, data models, and curation tools for neural circuit data, and we will incentivize expert contributions by arranging short reviews coupled to computable assertions. The knowledge graph will be used by local AI experts, published on the internet, and tested by a hackathon.