Model organism neural circuit knowledge graph

模型生物神经回路知识图谱

• 批准号: 10412857
• 负责人: CAROL J BULT
• 金额: $ 25.95万
• 依托单位: CALIFORNIA INSTITUTE OF TECHNOLOGY
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-18 至 2024-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10412857
• 关键词: Anatomy; Animal Model; Artificial Intelligence; Back; Behavior; Biochemical Pathway; Bioinformatics; Biological; Caenorhabditis elegans; California; Coupled; Data; Data Store; Databases; Development; Future; Gene Expression; Generations; Genes; Graph; Incentives; Infrastructure; Internet; Knowledge; Laboratories; Machine Learning; Methods; Modeling; Mus; Nervous system structure; Neurobiology; Neurons; Neuropeptide Receptor; Neuropeptides; Neurophysiology - biologic function; Neurosciences; Ontology; Output; Pathway interactions; Persons; Phenotype; Publishing; Quality Control; Research; Research Personnel; Retina; Specific qualifier value; Structure; Synapses; System; Testing; Vocabulary; WormBase; application programming interface; base; cell type; connectome; data access; data curation; data modeling; efficacy testing; genome resource; hackathon; improved; interest; knowledge graph; neural circuit; prototype; receptor; single-cell RNA sequencing; small molecule; tool; virtual

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.

项目摘要 为了从与神经回路相关的广泛但不同和孤立的数据中提取更大的价值，我们将利用 基因组资源联盟的本体论，生物信息学和策划人为人造 智能（AI）就绪知识图。使用AI进行神经的计算神经科学家的参与 电路分析将有助于指定知识图的形式，演示图的实用性，扩展 这些图表从秀丽隐杆线虫的定义明确的神经系统到类似的良好 定义的小鼠视网膜，并与开始将AI应用于神经的神经科学研究人员联系 电路。已知的实体（例如神经元，小分子和神经肽）和本体（解剖学，， 关系和实验证据）提供了图的基本数据。精选的断言提供了 知识，例如神经元之间的突触或功能连接，神经肽具有特定的受体，或 神经肽在特定的神经元中表达）。在此图模型中，实体是节点，本体论 关系是边缘。这些提供了由证据支持的推断知识图 断言。这种类型的知识图可以应用于基于表型的生物学途径 观察结果包括表达，神经元活动和生物行为，而不是物理相互作用 或酶活性，例如用于描述生化途径的活性。实现产生 知识图，我们将完善相关词汇，以专注于神经回路研究中使用的关系， 我们将调整现有的基础架构，以处理适当的本体论，数据模型和策展工具 神经电路数据，我们将通过安排与可计算的简短评论来激励专家的贡献 断言。知识图将由本地AI专家使用，在Internet上发布，并由A进行测试 黑客马拉松。