ExEmPLAR (Extracting, Exploring, and Embedding Pathways Leading to Actionable Research): a User-friendly Interface for Knowledge Graph Mining

Overview

Journal Bioinformatics

Publisher Oxford University Press

Specialty Biology

Date 2024 Jan 4

PMID 38175789

Authors

Jon-Michael T Beasley

Daniel R Korn

Nyssa N Tucker

Erick T M Alves

Eugene N Muratov

Chris Bizon

Alexander Tropsha

Affiliations

Soon will be listed here.

Abstract

Summary: Knowledge graphs are being increasingly used in biomedical research to link large amounts of heterogenous data and facilitate reasoning across diverse knowledge sources. Wider adoption and exploration of knowledge graphs in the biomedical research community is limited by requirements to understand the underlying graph structure in terms of entity types and relationships, represented as nodes and edges, respectively, and learn specialized query languages for graph mining and exploration. We have developed a user-friendly interface dubbed ExEmPLAR (Extracting, Exploring, and Embedding Pathways Leading to Actionable Research) to aid reasoning over biomedical knowledge graphs and assist with data-driven research and hypothesis generation. We explain the key functionalities of ExEmPLAR and demonstrate its use with a case study considering the relationship of Trypanosoma cruzi, the etiological agent of Chagas disease, to frequently associated cardiovascular conditions.

Availability And Implementation: ExEmPLAR is freely accessible at https://www.exemplar.mml.unc.edu/. For code and instructions for the using the application, see: https://github.com/beasleyjonm/AOP-COP-Path-Extractor.

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