Model organism neural circuit knowledge graph

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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.