Increased discoverability of rare disease datasets through knowledge graph integration.

Objectives: Demonstrate a methodology for improving discoverability of rare disease datasets by enriching source data with biological associations.

Materials And Methods: We developed an extension of the Biolink semantic model to incorporate patient data and generated a knowledge graph (KG) comprising patient data and associations between biological entities in an existing KG, leveraging existing mappings and mapping standards.

Results: The enriched model of patient data can support a search application that is aware of biological associations and provides a semantic search interface to discover and summarize patient datasets within the broader biological context.

The Monarch Initiative in 2024: an analytic platform integrating phenotypes, genes and diseases across species.

Bridging the gap between genetic variations, environmental determinants, and phenotypic outcomes is critical for supporting clinical diagnosis and understanding mechanisms of diseases. It requires integrating open data at a global scale. The Monarch Initiative advances these goals by developing open ontologies, semantic data models, and knowledge graphs for translational research.

Computing on Phenotypic Descriptions for Candidate Gene Discovery and Crop Improvement.

Many newly observed phenotypes are first described, then experimentally manipulated. These language-based descriptions appear in both the literature and in community datastores. To standardize phenotypic descriptions and enable simple data aggregation and analysis, controlled vocabularies and specific data architectures have been developed.

Automated Methods Enable Direct Computation on Phenotypic Descriptions for Novel Candidate Gene Prediction.

Natural language descriptions of plant phenotypes are a rich source of information for genetics and genomics research. We computationally translated descriptions of plant phenotypes into structured representations that can be analyzed to identify biologically meaningful associations. These representations include the entity-quality (EQ) formalism, which uses terms from biological ontologies to represent phenotypes in a standardized, semantically rich format, as well as numerical vector representations generated using natural language processing (NLP) methods (such as the bag-of-words approach and document embedding).