John H. Gennari

TabularQual provides a spreadsheet-based format for annotating logical models, with tools to convert between spreadsheets and SBML-qual, enhancing model reuse and interoperability.

KGBN enhances logical gene regulatory networks by integrating knowledge graphs and experimental data to create context-aware models for drug response prediction in AML.

QNPtoVox maps 2D quantitative neuropathology data to 3D MNI voxel space for spatial standardization and integration with neuroimaging tools.

The paper updates and corrects SED-ML files for 1055 models in BioModels to improve reproducibility and verification across simulation engines.

This paper uses topic modeling to identify developmental states in medulloblastoma subtypes across multiple datasets and technologies.

LM-Merger merges logical gene regulatory network models to improve coverage and predictive accuracy in disease contexts.

AMAS predicts annotations for SBML model elements using reference databases and similarity scores, achieving 80-95% accuracy for species and reactions.

NLIMED converts natural language queries into SPARQL to discover model entities from biosimulation model repositories.

SBMate evaluates annotation quality in systems biology models using coverage, consistency, and specificity metrics on 1,000 BioModels repository models.

The paper annotates biological models with semantic annotations and develops a web tool to help users discover relevant models in the Physiome Model Repository.

The paper introduces SBOL-OWL, an ontology that enables machine-readable semantic representation of synthetic genetic circuits, enhancing computational tools' ability to understand and validate designs.