Paper List
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Macroscopic Dominance from Microscopic Extremes: Symmetry Breaking in Spatial Competition
This paper addresses the fundamental question of how microscopic stochastic advantages in spatial exploration translate into macroscopic resource domi...
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Linear Readout of Neural Manifolds with Continuous Variables
This paper addresses the core challenge of quantifying how the geometric structure of high-dimensional neural population activity (neural manifolds) d...
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Theory of Cell Body Lensing and Phototaxis Sign Reversal in “Eyeless” Mutants of Chlamydomonas
This paper solves the core puzzle of how eyeless mutants of Chlamydomonas exhibit reversed phototaxis by quantitatively modeling the competition betwe...
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Cross-Species Transfer Learning for Electrophysiology-to-Transcriptomics Mapping in Cortical GABAergic Interneurons
This paper addresses the challenge of predicting transcriptomic identity from electrophysiological recordings in human cortical interneurons, where li...
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Uncovering statistical structure in large-scale neural activity with Restricted Boltzmann Machines
This paper addresses the core challenge of modeling large-scale neural population activity (1500-2000 neurons) with interpretable higher-order interac...
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Realizing Common Random Numbers: Event-Keyed Hashing for Causally Valid Stochastic Models
This paper addresses the critical problem that standard stateful PRNG implementations in agent-based models violate causal validity by making random d...
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A Standardized Framework for Evaluating Gene Expression Generative Models
This paper addresses the critical lack of standardized evaluation protocols for single-cell gene expression generative models, where inconsistent metr...
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Single Molecule Localization Microscopy Challenge: A Biologically Inspired Benchmark for Long-Sequence Modeling
This paper addresses the core challenge of evaluating state-space models on biologically realistic, sparse, and stochastic temporal processes, which a...
Open Biomedical Knowledge Graphs at Scale: Construction, Federation, and AI Agent Access with Samyama Graph Database
VaidhyaMegha Private Limited, India
30秒速读
IN SHORT: This paper addresses the core pain point of fragmented biomedical data by constructing and federating large-scale, open knowledge graphs to enable seamless cross-domain queries and natural language access via AI agents.
核心创新
- Methodology A reproducible ETL pattern for constructing large-scale biomedical KGs from heterogeneous public sources, featuring cross-source deduplication, batch loading, and portable snapshot export.
- Methodology Demonstration of cross-KG federation via property-based joins, enabling queries that traverse multiple independent knowledge graphs (e.g., from clinical trials to biological pathways).
- Methodology Schema-driven MCP server generation that automatically exposes typed tools for LLM agents, achieving 98% accuracy on a new BiomedQA benchmark, significantly outperforming text-to-Cypher (0%) and standalone GPT-4o (75%).
主要结论
- The federated graph (7.9M nodes, 28M edges) loads in approximately 3 minutes on commodity hardware (AWS g4dn.4xlarge, 62 GB RAM), with cross-KG queries completing in 80 ms–4 s.
- Schema-driven MCP tools achieve 98% accuracy (39/40) on the BiomedQA benchmark, dramatically outperforming text-to-Cypher (0%) and standalone GPT-4o (75%).
- A Rust native loader constructs the Drug Interactions KG (32,726 nodes, 191,970 edges) in under 1 second, demonstrating orders-of-magnitude performance improvement over Python HTTP-based ETL.
摘要: Biomedical knowledge is fragmented across siloed databases—Reactome for pathways, STRING for protein interactions, Gene Ontology for functional annotations, ClinicalTrials.gov for study registries, DrugBank for drug vocabularies, DGIdb for drug–gene interactions, SIDER for side effects, and dozens more. Researchers routinely download flat files from each source and write bespoke scripts to cross-reference them, a process that is slow, error-prone, and not reproducible. We present three open-source biomedical knowledge graphs—Pathways KG (118,686 nodes, 834,785 edges from 5 sources), Clinical Trials KG (7,774,446 nodes, 26,973,997 edges from 5 sources), and Drug Interactions KG (32,726 nodes, 191,970 edges from 3 sources)—built on Samyama, a high-performance graph database written in Rust. Our contributions are threefold. First, we describe a reproducible ETL pattern for constructing large-scale KGs from heterogeneous public data sources, with cross-source deduplication, batch loading (both Python Cypher and Rust native loaders), and portable snapshot export. Second, we demonstrate cross-KG federation: loading all three snapshots into a single graph tenant enables property-based joins across datasets, answering questions like “For drugs indicated for diabetes, what are their gene targets and which biological pathways do those targets participate in?”—a query that no single KG can answer alone. Third, we introduce schema-driven MCP server generation: each KG automatically exposes typed tools for LLM agents via the Model Context Protocol, enabling natural-language access to graph queries. We evaluate domain-specific MCP tools against text-to-Cypher and standalone GPT-4o on a new BiomedQA benchmark (40 pharmacology questions), achieving 98% accuracy vs. 0% for text-to-Cypher and 75% for standalone GPT-4o. All data sources are open-license (CC BY 4.0, CC0, OBO, public domain). Snapshots, ETL code, and MCP configurations are publicly available. The combined federated graph (7.9M nodes, 28M edges) loads in approximately 3 minutes from portable snapshots on commodity cloud hardware (AWS g4dn.4xlarge, 62 GB RAM), and cross-KG queries complete in 80 ms–4 s.