Tiffany J. Callahan

BIFO: A Biological Information Flow Ontology for Directed Propagation in Heterogeneous Biomedical Knowledge Graphs

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

Interpretable Machine Learning Uncovers Structural Determinants of Wnt-Wls Binding Specificity from Extended Atomistic Simulations

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

SAIR: Enabling deep learning for protein-ligand lnteractions with a synthetic structural dataset

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

The Unified Phenotype Ontology (uPheno): A framework for cross-species integrative phenomics

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

Causal feature selection using a knowledge graph combining structured knowledge from the biomedical literature and ontologies: a use case studying depression as a risk factor for Alzheimer's disease

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: A large-scale unifying knowledge graph framework for integrating biomolecular and biomedical data

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

Unifying the Identification of Biomedical Entities with the Bioregistry

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

The GA4GH Phenopacket schema: A computable representation of clinical data for precision medicine

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

KG-COVID-19: a framework to produce customized knowledge graphs for COVID-19 response

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

Applying knowledge-driven mechanistic inference totoxicogenomics

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

A Framework for Automated Construction of Heterogeneous Large-Scale Biomedical Knowledge Graphs

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

Semantic Integration of Clinical Laboratory Tests from Electronic Health Records for Deep Phenotyping and Biomarker Discovery

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