ArticleGenome biology2011
BioGraph: unsupervised biomedical knowledge discovery via automated hypothesis generation.
Article in Genome biology, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 58 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.
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Who cites it
58 citing papers in PubMed, 1 synthesis or guideline pooled it.
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- Data-driven hypothesis generation among inexperienced clinical researchers: A comparison of secondary data analyses with visualization (VIADS) and other tools.Journal of clinical and translational science · 2024Article
- iTraNet: a web-based platform for integrated trans-omics network visualization and analysis.Bioinformatics advances · 2024Article
- Potential Schizophrenia Disease-Related Genes Prediction Using Metagraph Representations Based on a Protein-Protein Interaction Keyword Network: Framework Development and Validation.JMIR formative research · 2023Article
- Data-driven hypothesis generation among inexperienced clinical researchers: A comparison of secondary data analyses with visualization (VIADS) and other tools.medRxiv : the preprint server for health sciences · 2023Article
- GediNET for discovering gene associations across diseases using knowledge based machine learning approach.Scientific reports · 2022Article
- Mitochondria Dysfunction-Mediated Molecular Subtypes and Gene Prognostic Index for Prostate Cancer Patients Undergoing Radical Prostatectomy or Radiotherapy.Frontiers in oncology · 2022Article
- CROssBAR: comprehensive resource of biomedical relations with knowledge graph representations.Nucleic acids research · 2021Article
- Preclinical validation of therapeutic targets predicted by tensor factorization on heterogeneous graphs.Scientific reports · 2020Article
- Mutated ATP10B increases Parkinson's disease risk by compromising lysosomal glucosylceramide export.Acta neuropathologica · 2020Article
- Gene Prioritization through Consensus Strategy, Enrichment Methodologies Analysis, and Networking for Osteosarcoma Pathogenesis.International journal of molecular sciences · 2020Article
- Structured reviews for data and knowledge-driven research.Database : the journal of biological databases and curation · 2020Article
- ROBOKOP: an abstraction layer and user interface for knowledge graphs to support question answering.Bioinformatics (Oxford, England) · 2019Article
- How to increase our belief in discovered statistical interactions via large-scale association studies?Human genetics · 2019Review
- Predicting the Correlation of EZH2 and Cancer Stem Cell Markers in Esophageal Squamous Cell Carcinoma.Journal of gastrointestinal cancer · 2018Article
- Variant information systems for precision oncology.BMC medical informatics and decision making · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We present BioGraph, a data integration and data mining platform for the exploration and discovery of biomedical information. The platform offers prioritizations of putative disease genes, supported by functional hypotheses. We show that BioGraph can retrospectively confirm recently discovered disease genes and identify potential susceptibility genes, outperforming existing technologies, without requiring prior domain knowledge. Additionally, BioGraph allows for generic biomedical applications beyond gene discovery. BioGraph is accessible at http://www.biograph.be.
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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.