Klarigi: Characteristic Explanations for Semantic Data

Abstract

Semantic annotation facilitates the use of background knowledge in analysis. This includes approaches that sort entities into groups, clusters, or assign labels or outcomes that are typically difficult to derive semantic explanations for. We introduce Klarigi, a tool that creates semantic explanations for groups of entities described by ontology terms implemented in a manner that balances multiple scoring heuristics. We demonstrate Klarigi by using it to identify characteristic terms for text-derived phenotypes of emergency admissions for two frequently conflated diagnoses, pulmonary embolism and pneumonia. Klarigi provides a universal method by which entity groups or labels can be explained semantically, and thus contributes to improved explainability of analysis methods.

GA4GH Phenopackets: A Practical Introduction

“…[13] We show examples of how this can be done in Java and C++ in the main phenopacket schema repository [23] and are preparing a Java library for validation and convenient construction of phenopackets that we will present separately. [24] Additionally, the formats and standards developed for phenopackets are increasingly being adopted by other data discovery and exchange protocols such as the GA4GH Beacon API, [25] as well as several tools and databases that consume or output phenopackets, [26][27][28][29][30][31] thereby promoting a wider penetration of phenopackets standards and practices throughout biomedical research.…”