The Human Phenotype Ontology in 2024: phenotypes around the world

\ua9 The Author(s) 2023. Published by Oxford University Press on behalf of Nucleic Acids Research. The Human Phenotype Ontology (HPO) is a widely used resource that comprehensively organizes and defines the phenotypic features of human disease, enabling computational inference and supporting genomic and phenotypic analyses through semantic similarity and machine learning algorithms. The HPO has widespread applications in clinical diagnostics and translational research, including genomic diagnostics, gene-disease discovery, and cohort analytics. In recent years, groups around the world have developed translations of the HPO from English to other languages, and the HPO browser has been internationalized, allowing users to view HPO term labels and in many cases synonyms and definitions in ten languages in addition to English. Since our last report, a total of 2239 new HPO terms and 49235 new HPO annotations were developed, many in collaboration with external groups in the fields of psychiatry, arthrogryposis, immunology and cardiology. The Medical Action Ontology (MAxO) is a new effort to model treatments and other measures taken for clinical management. Finally, the HPO consortium is contributing to efforts to integrate the HPO and the GA4GH Phenopacket Schema into electronic health records (EHRs) with the goal of more standardized and computable integration of rare disease data in EHRs

Initial achievements in relation extraction from RNA-focused scientific papers

Relation extraction from the scientific literature to comply with a domain ontology is a well-known problem in natural language processing and is particularly critical in precision medicine. The advent of large language models (LLMs) has paved the way for the development of new effective approaches to this problem, but the extracted relations can be affected by issues such as hallucination, which must be minimized. In this paper, we present the initial design and preliminary experimental validation of SPIREX, an extension of the SPIRES-based system for the extraction of RDF triples from scientific literature involving RNA molecules. Our system exploits schema constraints in the formulations of LLM prompts along with our RNA-based KG, RNA-KG, for evaluating the plausibility of the extracted triples. RNA-KG contains more than 9M edges representing different kinds of relationships in which RNA molecules can be involved. Initial experimental results on a controlled data set are quite encouraging