ArticleNature communications2019
A machine-compiled database of genome-wide association studies.
Article in Nature communications, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 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
11 citing papers in PubMed.
- Human genomic regions of systemic interindividual epigenetic variation are implicated in neurodevelopmental and metabolic disorders.medRxiv : the preprint server for health sciences · 2025Article
- The Type 2 Diabetes Knowledge Portal: An open access genetic resource dedicated to type 2 diabetes and related traits.Cell metabolism · 2023Article
- MetaboListem and TABoLiSTM: Two Deep Learning Algorithms for Metabolite Named Entity Recognition.Metabolites · 2022Article
- Molecular Pathology of Lung Cancer.Cold Spring Harbor perspectives in medicine · 2022Review
- HLA-SPREAD: a natural language processing based resource for curating HLA association from PubMed abstracts.BMC genomics · 2022Article
- Current trends on the application of artificial intelligence in medical sciences.Bioinformation · 2022Article
- Ontology-driven weak supervision for clinical entity classification in electronic health records.Nature communications · 2021Article
- GPCards: An integrated database of genotype-phenotype correlations in human genetic diseases.Computational and structural biotechnology journal · 2021Article
- GenomeChronicler: The Personal Genome Project UK Genomic Report Generator Pipeline.Frontiers in genetics · 2020Article
- LitGen: Genetic Literature Recommendation Guided by Human Explanations.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2020Article
- Snorkel: rapid training data creation with weak supervision.The VLDB journal : very large data bases : a publication of the VLDB Endowment · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Tens of thousands of genotype-phenotype associations have been discovered to date, yet not all of them are easily accessible to scientists. Here, we describe GWASkb, a machine-compiled knowledge base of genetic associations collected from the scientific literature using automated information extraction algorithms. Our information extraction system helps curators by automatically collecting over 6,000 associations from open-access publications with an estimated recall of 60-80% and with an estimated precision of 78-94% (measured relative to existing manually curated knowledge bases). This system represents a fully automated GWAS curation effort and is made possible by a paradigm for constructing machine learning systems called data programming. Our work represents a step towards making the curation of scientific literature more efficient using automated systems.
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.