The Medical Action Ontology: A tool for annotating and analyzing treatments and clinical management of human disease.

Background: Navigating the clinical literature to determine the optimal clinical management for rare diseases presents significant challenges. We introduce the Medical Action Ontology (MAxO), an ontology specifically designed to organize medical procedures, therapies, and interventions.

Methods: MAxO incorporates logical structures that link MAxO terms to numerous other ontologies within the OBO Foundry.

The Medical Action Ontology: A Tool for Annotating and Analyzing Treatments and Clinical Management of Human Disease.

Navigating the vast landscape of clinical literature to find optimal treatments and management strategies can be a challenging task, especially for rare diseases. To address this task, we introduce the Medical Action Ontology (MAxO), the first ontology specifically designed to organize medical procedures, therapies, and interventions in a structured way. Currently, MAxO contains 1757 medical action terms added through a combination of manual and semi-automated processes.

Interpretable Clinical Genomics with a Likelihood Ratio Paradigm.

Human Phenotype Ontology (HPO)-based analysis has become standard for genomic diagnostics of rare diseases. Current algorithms use a variety of semantic and statistical approaches to prioritize the typically long lists of genes with candidate pathogenic variants. These algorithms do not provide robust estimates of the strength of the predictions beyond the placement in a ranked list, nor do they provide measures of how much any individual phenotypic observation has contributed to the prioritization result.

Representing glycophenotypes: semantic unification of glycobiology resources for disease discovery.

While abnormalities related to carbohydrates (glycans) are frequent for patients with rare and undiagnosed diseases as well as in many common diseases, these glycan-related phenotypes (glycophenotypes) are not well represented in knowledge bases (KBs). If glycan-related diseases were more robustly represented and curated with glycophenotypes, these could be used for molecular phenotyping to help to realize the goals of precision medicine. Diagnosis of rare diseases by computational cross-species comparison of genotype-phenotype data has been facilitated by leveraging ontological representations of clinical phenotypes, using Human Phenotype Ontology (HPO), and model organism ontologies such as Mammalian Phenotype Ontology (MP) in the context of the Monarch Initiative.

The Monarch Initiative in 2019: an integrative data and analytic platform connecting phenotypes to genotypes across species.

In biology and biomedicine, relating phenotypic outcomes with genetic variation and environmental factors remains a challenge: patient phenotypes may not match known diseases, candidate variants may be in genes that haven't been characterized, research organisms may not recapitulate human or veterinary diseases, environmental factors affecting disease outcomes are unknown or undocumented, and many resources must be queried to find potentially significant phenotypic associations. The Monarch Initiative (https://monarchinitiative.org) integrates information on genes, variants, genotypes, phenotypes and diseases in a variety of species, and allows powerful ontology-based search.

Semantic integration of clinical laboratory tests from electronic health records for deep phenotyping and biomarker discovery.

Electronic Health Record (EHR) systems typically define laboratory test results using the Laboratory Observation Identifier Names and Codes (LOINC) and can transmit them using Fast Healthcare Interoperability Resource (FHIR) standards. LOINC has not yet been semantically integrated with computational resources for phenotype analysis. Here, we provide a method for mapping LOINC-encoded laboratory test results transmitted in FHIR standards to Human Phenotype Ontology (HPO) terms.

High Throughput NPY-Venus and Serotonin Secretion Assays for Regulated Exocytosis in Neuroendocrine Cells.

Here we describe two assays to measure dense core vesicle (DCV) exocytosis-mediated cargo secretion in neuroendocrine cells. To conduct siRNA screens for novel genes in regulated DCV exocytosis, we developed a plate reader-based secretion assay using DCV cargo, NPY-Venus, and an orthogonal H-serotonin secretion assay. The NPY-Venus secretion assay was successfully used for a high throughput siRNA screen, and the serotonin secretion assay was used to validate hits identified from the screen (Sorensen, 2017; Zhang , 2017).