Yongqun Oliver He

The paper uses Vaccine Ontology to model immune biomarkers, profiles, and responses, enhancing classification and analysis of vaccine-induced immunity.

Ignet 2.0 and Vignet provide ontology-driven platforms for gene interaction discovery, vaccine network analysis, and AI-assisted knowledge retrieval from biomedical literature.

The paper introduces the Vaccine Ontology (VO), a standardized framework for representing vaccines and related data, enhancing data integration and AI research.

The paper introduces a common data model and databases for integrating heterogeneous biomedical data, enabling cross-domain analysis and revealing sex-specific immune response insights.

This paper introduces VaxjoGNN, a graph neural network for recommending vaccine adjuvants based on biomedical ontologies, achieving significant improvements over random baseline.

The paper presents a multi-modal atlas of human kidney disease, identifying cellular and spatial mechanisms of injury resolution or failure and linking them to clinical outcomes.

This paper develops VaxKG, a vaccine knowledge graph integrating VIOLIN and VO, enabling complex queries and LLM-powered chat systems for vaccine information.

The paper maps CVX vaccine codes to the Vaccine Ontology using semi-automated and manual methods, achieving full coverage and improving semantic vaccine classification.

This study uses large language models and ontologies to extract and explain protein-protein interactions from scientific literature, showing improved performance with parameter-efficient fine-tuning.

This paper describes the construction and usage of the Human Reference Atlas (HRA) v2.0, which integrates multiscale data across 36 organs and provides tools for analysis and visualization.

VaxLLM automates Brucella vaccine article classification and annotation with high accuracy, outperforming non-fine-tuned models and enabling structured data integration.

This study enhances Vaxign-DL by integrating ESM-generated features, showing improved performance in predicting bacterial vaccine antigens.

The paper uses ontologies to integrate clinical, pathological, and molecular kidney data for precision medicine analysis.

This paper introduces Vaxign-DL, a deep learning method for vaccine design, which achieves high accuracy in predicting vaccine candidates from bacterial sequences.

The paper identifies and analyzes hosts of human coronaviruses, finding all verified natural and laboratory hosts are therian mammals, and provides a standardized ontology for host knowledge.

The paper constructs a reference tissue atlas of the human kidney using multi-omics data to identify cell types, pathways, and genes across nephron segments and interstitium.

This study designs SARS-CoV-2 spike protein variants with enhanced immunogenicity by engineering T cell epitopes while preserving structure and function.

The paper predicts COVID-19 vaccine candidates using reverse vaccinology and machine learning, identifying the N and nsp3 proteins as potential targets.