cs.AIMar 16, 2026

Knowledge Graph Extraction from Biomedical Literature for Alkaptonuria Rare Disease

Authors: Giang Pham, Rebecca Finetti, Caterina Graziani, Bianca Roncaglia, Asma Bendjeddou, Linda Brodo, Sara Brunetti, Moreno Falaschi, 

Organizations: Department of Computer Science, University of Pisa, Pisa, Italy. · Department of Biotechnology, Chemistry and Pharmacy, University of Siena, Siena, Italy · Department of Information Engineering and Mathematics, University of Siena, Siena, Italy · Department of Scienze Economiche e Aziendali, University of Sassari, Sassari, Italy. · 

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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.

Figures & tables

Figure 1: The high-confidence network : KG containing only relations that appear in at least two publications. An HTML view of the network is available at the link .
Figure 2: Comparison between the two KGs and the gene–gene connections from STRING .
Figure 3: Comparison between our KGs and gene–drug connections from DGIdb .
Figure 4: Comparison between the KGs and the network derived from the tyrosine metabolism pathway in KEGG .
Figure 5: Distribution of node degrees and clustering coefficients
Figure 6: Community detection. AKU communities extracted by different community detection approaches.
Figure 7: Nodes with highest PPR and Katz centrality score from the two KGs.
Figure 8: Top chemicals with the highest HeteSim scores with respect to Alkaptonuria .
Figure 9: Main connected component of the network derived from combining the red and green edges of Figures 2(c) and 3(c) (only genes and chemicals).
Figure 10: Top diseases and chemicals similar to Alkaptonuria and nitisinone.
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