Nicole Vasilevsky

Pan-human Azimuth maps diverse human cells into a unified hierarchical typology, enabling cross-tissue comparisons and spatial transcriptomic annotation.

The paper evaluates two self-phenotyping tools, showing that GenomeConnect provides more accurate phenotype profiles while Phenotypr offers greater precision and user preference for its autocomplete format.

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

The paper curates 111 gene-disease relationships for syndromic disorders, classifying their clinical validity and identifying gaps in knowledge.

This paper introduces OBA, a formalized ontology for biological attributes to facilitate data integration across phenotypic, genetic, and environmental data.

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

The paper describes how the OBO Foundry formalized its principles into operational rules and implemented automated checks to evaluate ontology compliance, improving interoperability and quality.

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

The paper creates synthetic clinical cardiovascular data and a curriculum to teach data science fundamentals, finding mixed student performance in applying machine learning techniques.

The paper describes the Monarch Initiative, a platform that integrates multimodal data across species to enhance genotype-phenotype analysis and precision medicine.

The paper introduces the Monarch Initiative, a resource that unifies genomic and phenotypic data across species to improve understanding of health and disease.