ArticleBioinformatics (Oxford, England)2025
KGG: a fully automated workflow for creating disease-specific knowledge graphs.
Article in Bioinformatics (Oxford, England), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Target identification and assessment in the era of AI.Nature reviews. Drug discovery · 2026Review
- Potential and Limitations of Large Language Models for Medical Literature Analysis: A Preliminary Investigation.Cureus · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
motivationKnowledge graphs (KGs) in life sciences have become an important application of systems biology as they delineate complex biological and pathophysiological phenomena. They are composed of biological and chemical entities represented with standard ontologies to comply with Findable, Accessible, Interoperable and Reusable (FAIR) principles. Alongside serving as a graph database, KGs hold the potential to address complex scientific queries and facilitate downstream analyses. However, the process of constructing KGs is expensive and time consuming as it primarily relies on manual curation from published literature and experimental data. The existing text-mining workflows are still in their infancy and fail to achieve the accuracy and reliability of manual curation.
resultsKnowledge graph generator (KGG) is an automated workflow for representing chemotype and phenotype of diseases and medical conditions. It embeds the underlying schema of curated databases such as OpenTargets, Uniprot, ChEMBL, Integrated Interactions Database and GWAS Central resembling a clockwork-esque mechanism. The resultant KG is a comprehensive and rational assembly of disease-associated entities such as proteins, protein-related pathways, biological processes and functions, genetic variants, chemicals, mechanism of actions, assays and adverse effects. As use cases, we have used KGs to identify shared entities for possible link of comorbidity and compared them with KGs from other sources. We have also demonstrated a use case of identifying putative new targets and repurposing drug candidates in Parkinson's Disease. Lastly, we have developed reusable workflows to explore drug-likeness of chemicals and identify structures of proteins. AVAILABILITY AND IMPLEMENTATION: The resources and codes for KGG are publicly available at: https://github.com/Fraunhofer-ITMP/kgg.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.