ArticleScientific data2024
An open source knowledge graph ecosystem for the life sciences.
Article in Scientific data, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 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
36 citing papers in PubMed.
- Interpreting human genetic variation at atomic resolution.Nature genetics · 2026Review
- VITAGRAPH: building a knowledge graph for biologically relevant learning tasks.Scientific data · 2026Article
- MetagenomicKG: a knowledge graph for metagenomic applications.Bioinformatics (Oxford, England) · 2026Article
- A Pilot Project Leveraging Large Language Models for Automated Screening and Variable Extraction in Observational Studies.medRxiv : the preprint server for health sciences · 2026Article
- Knowledge Graph-Driven AI in Biohealth: From Biomedical Discovery to Health Risk Prediction.Delaware journal of public health · 2026Article
- RNA-KG v2.0: an RNA-centered Knowledge Graph with Properties.NAR genomics and bioinformatics · 2026Article
- SPHN Connector - a scalable pipeline for generating validated knowledge graphs from federated and semantically enriched health data.BMC medical informatics and decision making · 2026Article
- Design and evaluation of semantically-valid negative samples integration techniques for scalable semi-automated drug repurposing prediction pipelines in rare disease research.BMC bioinformatics · 2026Article
- KG-Microbe: Building modular and scalable knowledge graphs for microbiome and microbial sciences.GigaScience · 2026Article
- Desiderata for a biomedical knowledge network: opportunities, challenges and future directions.Bioinformatics advances · 2026Article
- A multimodal vision knowledge graph of cardiovascular disease.Nature cardiovascular research · 2026Article
- Structural and Functional Impacts of SARS-CoV-2 Spike Protein Mutations: Insights From Predictive Modeling and Analytics.JMIR bioinformatics and biotechnology · 2025Article
- NOCTIS: open-source toolkit that turns reaction data into actionable graph networks.Journal of cheminformatics · 2025Article
- Large Language Models in Bio-Ontology Research: A Review.Bioengineering (Basel, Switzerland) · 2025Review
- The Data Distillery: A Graph Framework for Semantic Integration and Querying of Biomedical Data.bioRxiv : the preprint server for biology · 2025Article
- Construction of intelligent decision support systems through integration of retrieval-augmented generation and knowledge graphs.Scientific reports · 2025Article
- Desiderata for a biomedical knowledge network: opportunities, challenges and future Directions.ArXiv · 2025Article
- ORMCKB: A Knowledge Database for Personalized Medicine in Deciphering the Oral Microbiome-Disease Axis.Interdisciplinary sciences, computational life sciences · 2025Article
- An epidemiological knowledge graph extracted from the World Health Organization's Disease Outbreak News.Scientific data · 2025Article
- Enriched knowledge representation in biological fields: a case study of literature-based discovery in Alzheimer's disease.Journal of biomedical semantics · 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
32 authors.
Funding
Abstract
Translational research requires data at multiple scales of biological organization. Advancements in sequencing and multi-omics technologies have increased the availability of these data, but researchers face significant integration challenges. Knowledge graphs (KGs) are used to model complex phenomena, and methods exist to construct them automatically. However, tackling complex biomedical integration problems requires flexibility in the way knowledge is modeled. Moreover, existing KG construction methods provide robust tooling at the cost of fixed or limited choices among knowledge representation models. PheKnowLator (Phenotype Knowledge Translator) is a semantic ecosystem for automating the FAIR (Findable, Accessible, Interoperable, and Reusable) construction of ontologically grounded KGs with fully customizable knowledge representation. The ecosystem includes KG construction resources (e.g., data preparation APIs), analysis tools (e.g., SPARQL endpoint resources and abstraction algorithms), and benchmarks (e.g., prebuilt KGs). We evaluated the ecosystem by systematically comparing it to existing open-source KG construction methods and by analyzing its computational performance when used to construct 12 different large-scale KGs. With flexible knowledge representation, PheKnowLator enables fully customizable KGs without compromising performance or usability.
Indexed as
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What Socratic 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 Socratic graph.