Charles Tapley Hoyt

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

OmniPath integrates molecular knowledge from 168 resources to support multi-omics analysis, offering curated data, predictions, and tools for causal modeling across bulk, single-cell, and spatial data.

The paper introduces SeMRA, a tool for integrating biomedical identifier mappings across resources, enabling large-scale data interoperability.

Biomappings predicts and curates missing biomedical identifier mappings between ontologies, providing curated and predicted mappings for use in data integration.

The paper introduces the Bioregistry, an open metaregistry that integrates and expands existing biomedical entity registries to improve interoperability and data reuse.

The paper introduces STonKGs, a Transformer model trained on biomedical text and knowledge graphs, which outperforms single-modality models in classification tasks.

Gilda is a tool that normalizes biomedical entity names using machine-learned disambiguation models and provides a web service for this task.

CLEP generates patient representations by integrating prior biological knowledge with patient data, improving classification performance compared to using raw transcriptomics data.

GuiltyTargets prioritizes therapeutic targets using deep learning on protein interaction networks, achieving high accuracy in disease-specific target ranking and showing potential for target repositioning.

The paper develops an ontology and knowledge graph to model heme biology and investigates its role in Toll-like receptor signaling.

This study benchmarks the impact of pathway database choice on enrichment analysis and predictive modeling, showing that using multiple or integrative databases improves consistency and performance.

PathMe harmonizes three pathway databases into a unified schema to enable comprehensive exploration of pathway crosstalk and consensus.

The paper introduces Bio2BEL, a set of Python packages that convert multi-modal biological data into Biological Expression Language for integration and analysis.

BioKEEN provides a tool for learning and evaluating biological knowledge graph embeddings with an interactive interface and a case study on pathway crosstalk prediction.

The paper develops workflows to re-curate and enrich knowledge graphs encoded in Biological Expression Language using automated and manual methods.

ComPath curates pathway mappings between databases and provides tools for exploring and analyzing pathway knowledge, demonstrating its utility in identifying biological insights in Parkinson's disease.

The paper identifies shared genetic and mechanistic bases between epilepsy and its comorbidities using literature mining and knowledge assembly.

BEL Commons provides a web environment for analyzing and exploring networks encoded in Biological Expression Language, enabling users to upload, validate, visualize, and analyze biological networks.