ArticleScientific data2023
Building a knowledge graph to enable precision medicine.
Article in Scientific data, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 172 papers, 1 of them a synthesis that pooled it.
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
172 citing papers in PubMed, 1 synthesis or guideline pooled it, 389 citations in OpenAlex.
- Visible neural networks for multi-omics integration: a critical review.Frontiers in artificial intelligence · 2025Pooled it
- AI semantics for biomedical data integration.bioRxiv : the preprint server for biology · 2026Article
- IMF-DDI: Information Mapping and Fusion Framework for Drug-drug Interaction Prediction.Interdisciplinary sciences, computational life sciences · 2026Article
- GN-Net: A Geometric and Neighborhood-Aware Network for Predicting Intracranial Aneurysm Rupture Risk and Assisting Clinical Decision-Making.Interdisciplinary sciences, computational life sciences · 2026Article
- CAREPath: semantic context-aware reasoning paths with mechanism-augmented embeddings for drug repurposing.Briefings in bioinformatics · 2026Article
- Agentic systems in computational pathology: architectures, evidence, and translational challenges.Journal of translational medicine · 2026Review
- A unified framework and benchmark for generalizable biomedical knowledge extraction and applications with large language models.Cell reports. Medicine · 2026Article
- Graph-Based Machine Learning for Predicting Drug-Drug Interactions: A Systematic Review.Pharmaceuticals (Basel, Switzerland) · 2026Review
- Momentum contrast-enhanced multimodal representation learning for drug synergy prediction.Bioinformatics (Oxford, England) · 2026Article
- Literature-derived, context-aware gene regulatory networks improve biological predictions and mathematical modeling.Bioinformatics (Oxford, England) · 2026Article
- Benchmarking the impact of data leakage on the performance of knowledge graph embedding models for biomedical link prediction.Bioinformatics (Oxford, England) · 2026Article
- Enhancing link prediction in biomedical knowledge graphs with BioPathNet.Nature biomedical engineering · 2026Article
- Causal graph neural networks for healthcare.Nature biomedical engineering · 2026Review
- Microbiome and aging: Trajectories of microbiome age across human ecosystems and their systemic effects.iMeta · 2026Review
- PregMedNet: Multifaceted maternal medication impacts on neonatal complications.Nature communications · 2026Article
- Eras of bioinformatics technologies from command-line interfaces to artificial intelligence (AI) chatbots.Briefings in bioinformatics · 2026Review
- Paeonia suffruticosa root bark extract alleviates gut-brain axis dysfunction in DSS-induced colitis via suppression of intestinal necroptosis and glial activation.Inflammopharmacology · 2026Article
- LogosKG: Hardware-Optimized Scalable and Interpretable Knowledge Graph Retrieval.Proceedings of the conference. Association for Computational Linguistics. Meeting · 2026Article
- Target identification and assessment in the era of AI.Nature reviews. Drug discovery · 2026Review
- Bridging the Great Discordance: Ontological Intelligence as the New Paradigm in Spine Surgery.Neurospine · 2026Article
112 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Developing personalized diagnostic strategies and targeted treatments requires a deep understanding of disease biology and the ability to dissect the relationship between molecular and genetic factors and their phenotypic consequences. However, such knowledge is fragmented across publications, non-standardized repositories, and evolving ontologies describing various scales of biological organization between genotypes and clinical phenotypes. Here, we present PrimeKG, a multimodal knowledge graph for precision medicine analyses. PrimeKG integrates 20 high-quality resources to describe 17,080 diseases with 4,050,249 relationships representing ten major biological scales, including disease-associated protein perturbations, biological processes and pathways, anatomical and phenotypic scales, and the entire range of approved drugs with their therapeutic action, considerably expanding previous efforts in disease-rooted knowledge graphs. PrimeKG contains an abundance of 'indications', 'contradictions', and 'off-label use' drug-disease edges that lack in other knowledge graphs and can support AI analyses of how drugs affect disease-associated networks. We supplement PrimeKG's graph structure with language descriptions of clinical guidelines to enable multimodal analyses and provide instructions for continual updates of PrimeKG as new data become available.
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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.