Tiffany J. Callahan

The paper introduces BIFO, an ontology that specifies biologically admissible directed transformations in biomedical knowledge graphs to enable meaningful signal propagation.

The paper uses machine learning on molecular dynamics simulations to identify key residues determining Wnt-Wls binding specificity across four Wnt proteins.

The paper introduces SAIR, a synthetic dataset of protein-ligand structures with activity data, to improve deep learning models for binding affinity prediction.

The paper introduces uPheno, a framework for cross-species phenomics integration by harmonizing phenotype data across species and ontologies.

This paper introduces a knowledge graph approach to causal feature selection, identifying confounders, mediators, and colliders for depression's effect on Alzheimer's disease risk.

Petagraph integrates biomolecular data into a unified knowledge graph using UMLS ontologies to enable efficient multi-omics analysis across multiple datasets.

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 the GA4GH Phenopacket schema to standardize the exchange of phenotypic data for precision medicine applications.

KG-COVID-19 creates customizable knowledge graphs for COVID-19 response by integrating siloed biomedical data into structured formats for various research applications.

The paper introduces MechSpy, a framework that generates mechanistic hypotheses of toxicity by combining gene expression data with biological knowledge, and validates predictions experimentally.

The paper introduces PheKnowLator, a framework for constructing large-scale biomedical knowledge graphs, and evaluates its performance on human disease mechanisms.

The paper semantically integrates LOINC-encoded laboratory data with the Human Phenotype Ontology to identify asthma biomarkers from EHR data.