Knowledge Graph Extraction from Biomedical Literature for Alkaptonuria Rare Disease

TL;DR

This paper presents a text-mining approach to extract and construct knowledge graphs from biomedical literature to better understand the rare disease Alkaptonuria, revealing disease interactions and potential therapeutic targets.

Contribution

The study introduces a novel framework for large-scale biomedical relation extraction specifically applied to ultra-rare diseases like AKU, addressing data scarcity issues.

Findings

Constructed two validated knowledge graphs for AKU

Identified potential genes, diseases, and therapies related to AKU

Demonstrated systemic interactions and therapeutic targets for AKU

Abstract

Alkaptonuria (AKU) is an ultra-rare autosomal recessive metabolic disorder caused by mutations in the HGD (Homogentisate 1,2-Dioxygenase) gene, leading to a pathological accumulation of homogentisic acid (HGA) in body fluids and tissues. This leads to systemic manifestations, including premature spondyloarthropathy, renal and prostatic stones, and cardiovascular complications. Being ultra-rare, the amount of data related to the disease is limited, both in terms of clinical data and literature. Knowledge graphs (KGs) can help connect the limited knowledge about the disease (basic mechanisms, manifestations and existing therapies) with other knowledge; however, AKU is frequently underrepresented or entirely absent in existing biomedical KGs. In this work, we apply a text-mining methodology based on PubTator3 for large-scale extraction of biomedical relations. We construct two KGs of different sizes, validate them using existing biochemical knowledge and use them to extract genes, diseases and therapies possibly related to AKU. This computational framework reveals the systemic interactions of the disease, its comorbidities, and potential therapeutic targets, demonstrating the efficacy of our approach in analyzing rare metabolic disorders.