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Yuhan Dong

Publications and source records attributed to Yuhan Dong.

3 recordsLinked to original sources
PubMed2026

BioMedGraphica: an all-in-one platform for joint textual biomedical prior knowledge and numeric graph generation.

MOTIVATION: Multiomics data analysis is essential for scientific discovery in precision medicine. However, translating analysis results of omics data analysis into novel scientific hypotheses remains a significant challenge. Human experts must manually review analysis results and generate new hypotheses based on extensive and interconnected biomedical prior knowledge, which is subjective and not scalable. While large language models can accelerate the discovery, their reasoning improves when grounded in structured, auditable, and comprehensive biomedical prior knowledge. However, biomedical knowledge is scattered across heterogeneous databases that use diverse and inconsistent nomenclature systems, making it difficult to integrate resources into a unified format for scalable analysis. This fragmentation limits the ability of artificial intelligence systems to fully leverage biomedical data for scientific discovery. RESULTS: We developed BioMedGraphica, a novel all-in-one platform that harmonizes fragmented biomedical resources by integrating 11 entity types and 30 relation types from 43 databases into a unified textual prior knowledge graph containing 2 306 921 entities and 27 232 091 relations. In addition, we present a novel textual-numeric graph (TNG) data structure concept, where textual information captures prior biological knowledge (e.g. transcription start sites, functions, mechanisms), numeric values represent quantitative biomedical features, and the integrated relations can help uncover mechanisms. By bridging prior knowledge with user-specific data, TNG is a novel and ideal data structure for developing novel graph analysis models. AVAILABILITY AND IMPLEMENTATION: The code is available at: https://github.com/FuhaiLiAiLab/BioMedGraphica and BioMedGraphica knowledge graph database can be downloaded from huggingface dataset: https://huggingface.co/datasets/FuhaiLiAiLab/BioMedGraphica.

Humans↗
PubMed2025

PUS7-dependent Ψ reshapes specific synaptic gene exons to facilitate fear extinction memory formation.

RNA modifications serve as dynamic regulators of neural plasticity through their ability to fine-tune transcript stability and splicing. Pseudouridine (Ψ), an evolutionarily conserved RNA modification catalyzed by pseudouridine synthases, plays established roles in neurodevelopment, yet its functional significance in activity-dependent behavioral adaptation remains poorly defined. Here, we investigate Ψ-mediated epitranscriptomic regulation within the infralimbic prefrontal cortex (ILPFC), a brain region requiring precise synaptic remodeling for the clinically relevant form of fear extinction memory. Combining transcriptome-wide pseudouridylation profiling with behavioral analysis in mice, we identified selective Ψ enrichment at exons of synaptic regulatory genes within ILPFC during fear extinction learning. Fear extinction in the ILPFC drives concomitant exonic Ψ deposition and upregulation of synaptogenic transcripts, processes that involve pseudouridine synthase PUS7. Crucially, PUS7 knockdown in the ILPFC selectively impaired fear extinction memory formation without altering baseline fear expression, establishing a causal link between Ψ-dependent RNA processing and activity-dependent synaptic structural remodeling in this microcircuit. Our findings demonstrate that PUS7-mediated Ψ modification spatiotemporally regulates activity-dependent RNA dynamics in the ILPFC, providing the evidence that epitranscriptomic mechanisms precisely coordinate synaptic gene expression within behaviorally defined brain sub-region. This work bridges molecular RNA biology with systems neuroscience, revealing a novel mechanism for activity-dependent regulation of fear extinction in ILPFC.

Animals↗
PubMed2025

BioMedGraphica: An All-in-One Platform for Joint Textual Biomedical Prior Knowledge and Numeric Graph Generation.

Multi-omic data analysis is essential for scientific discovery in precision medicine. However, translating statistical results of omic data analysis into novel scientific hypothesis remains a significant challenge. Human experts must manually review analysis results and generate new hypothesis based on extensive and inter-connected biomedical prior knowledge, which is subjective and not scalable. While large language models (LLMs) can accelerate the discovery, their reasoning improves when grounded in structured, auditable and comprehensive biomedical prior knowledge. Biomedical knowledge, however, is scattered across heterogeneous databases that use diverse and inconsistent nomenclature systems, making it difficult to integrate resources into a unified format for scalable analysis. This fragmentation limits the ability of AI systems to fully leverage biomedical data for scientific discovery. To address these challenges, we developed BioMedGraphica , an all-in-one platform that harmonizes fragmented biomedical resources by integrating 11 entity types and 30 relation types from 43 databases into a unified knowledge graph containing 2,306,921 entities and 27,232,091 relations. In addition, to the best of our knowledge, this is the first work to propose a novel Textual-Numeric Graph (TNG) data-structure for multi-omics data analysis. In TNG, textual information captures prior biological knowledge (e.g., transcription start sites, functions, mechanisms), while numeric values represent quantitative biomedical features, and the integrated relations can help uncover mechanisms. By bridging prior knowledge with user-specific data, TNG is a novel and ideal data-structure for the development of graph foundation models, with the potential to improve prediction performance and interpretability, while also augmenting LLMs by supplying graph-structured mechanistic context to strengthen reasoning. The details for BioMedGraphica code can be accessed by github link: https://github.com/FuhaiLiAiLab/BioMedGraphica and BioMedGraphica knowledge graph data can be downloaded from huggingface dataset: https://huggingface.co/datasets/FuhaiLiAiLab/BioMedGraphica.

biomedical knowledge graph↗