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| title: NeuroOracle | |
| emoji: 🧠 | |
| colorFrom: indigo | |
| colorTo: blue | |
| sdk: docker | |
| app_port: 7860 | |
| pinned: false | |
| license: mit | |
| short_description: Dual-source knowledge graph for NeuroClaw | |
| tags: | |
| - neuroscience | |
| - knowledge-graph | |
| - neuroimaging | |
| - hypothesis-generation | |
| - autoresearch | |
| <div align="center"> | |
| # NeuroOracle | |
| **A dual-source knowledge graph and hypothesis engine — part of [NeuroClaw](https://github.com/CUHK-AIM-Group/NeuroClaw).** | |
| [](https://github.com/CUHK-AIM-Group/NeuroClaw) | |
| [](https://cuhk-aim-group.github.io/NeuroClaw/) | |
| [](https://arxiv.org/abs/2604.24696) | |
| [](https://github.com/CUHK-AIM-Group/NeuroClaw/blob/main/LICENSE) | |
| </div> | |
| --- | |
| ## What is NeuroOracle? | |
| NeuroOracle is the knowledge-graph component of **NeuroClaw**, an autonomous research framework for neuroimaging. It combines two complementary information sources to provide a quality-grounded foundation for hypothesis generation: | |
| 1. **Curated structured databases** — concepts and relations imported from NeuroNames, MeSH, DisGeNET, BrainMap, and Cognitive Atlas, all aligned to UMLS semantic types. | |
| 2. **PubMed-derived scientific claims** — evidence-weighted edges extracted from neuroimaging literature using LLM-based claim extractors, with provenance (PMID, p-value, sample size, study type) preserved on every edge. | |
| Together they form a graph of approximately **89K concept nodes** and **174K edges**, covering brain anatomy, diseases, genes, neurotransmitters, drugs, cognitive functions, imaging features, connectivity, visual stimuli, and emotion/vigilance labels. | |
| This Space provides an interactive explorer for browsing the graph, inspecting evidence chains behind individual claims, and visualising multi-hop hypothesis paths. | |
| ## Why dual-source matters | |
| Existing autoresearch systems either rely on free-form LLM ideation (no quality anchor) or on a single curated KG (limited coverage and stale evidence). NeuroOracle's dual-source design is what enables NeuroClaw to: | |
| - Generate hypotheses with **traceable evidence chains** back to specific PubMed papers | |
| - Filter or re-rank hypotheses using **evidence weights** (effect size, sample size, replicability) | |
| - Iterate the graph itself in response to new findings, rather than treating the KG as a static asset | |
| ## NeuroClaw ecosystem | |
| NeuroOracle is one of three modules within the broader NeuroClaw system: | |
| | Module | Role | | |
| |--------|------| | |
| | **NeuroClaw** | Top-level system: data processing, model execution, skill library (81 skills across 29 datasets) | | |
| | **NeuroOracle** | Knowledge graph and hypothesis engine (this Space) | | |
| | **NeuroBench** | Multi-agent neuroimaging workflow benchmark | | |
| NeuroClaw is the umbrella framework; NeuroOracle is its scientific memory; NeuroBench measures how effectively the agent can use that memory to do real research work. | |
| ## What you can do here | |
| - **Browse concepts** across 13 domain tags (neuroanatomy, disease, gene, drug, imaging_feature, connectivity, cognitive_function, visual_stimulus, emotion, vigilance, paradigm, dataset, ml_model) | |
| - **Inspect claims** — every PubMed-derived edge carries the source paper, predicate (`is_biomarker_of`, `predicts`, `correlates_with`, etc.), and structured evidence fields | |
| - **Trace hypothesis paths** — multi-hop reasoning examples such as `visual stimulus → functional ROI → anatomical region`, or `imaging feature → gene → disease` | |
| - **Filter subgraphs** by domain, dataset, or relation type for focused exploration | |
| ## Links | |
| - 🏠 **Project homepage**: <https://cuhk-aim-group.github.io/NeuroClaw/> | |
| - 💻 **Source code (GitHub)**: <https://github.com/CUHK-AIM-Group/NeuroClaw> | |
| - 📄 **Technical report (arXiv)**: <https://arxiv.org/abs/2604.24696> | |
| - 🧠 **NeuroOracle docs page**: <https://cuhk-aim-group.github.io/NeuroClaw/neuro-oracle.html> | |
| ## Citation | |
| If NeuroOracle or NeuroClaw is useful for your research, please cite the NeuroClaw technical report: | |
| ```bibtex | |
| @article{neuroclaw2026, | |
| title = {NeuroClaw: Closed-Loop Agentic AI for Executable and Reproducible Neuroimaging Research}, | |
| author = {NeuroClaw Team}, | |
| journal = {arXiv preprint arXiv:2604.24696}, | |
| year = {2026}, | |
| url = {https://arxiv.org/abs/2604.24696} | |
| } | |
| ``` | |
| ## License | |
| MIT — same as the NeuroClaw repository. See <https://github.com/CUHK-AIM-Group/NeuroClaw/blob/main/LICENSE> for full terms. | |
| ## Contact | |
| For questions or issues, please open an issue on the [NeuroClaw GitHub repository](https://github.com/CUHK-AIM-Group/NeuroClaw/issues). | |