ReviewBioData mining2016
Representing and querying disease networks using graph databases.
Review in BioData mining, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 2 of them syntheses 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
36 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Graph databases in systems biology: a systematic review.Briefings in bioinformatics · 2024Pooled it
- Graph-Representation of Patient Data: a Systematic Literature Review.Journal of medical systems · 2020Pooled it
- Enhanced Health Study Discoverability: Graph-Based Analysis Approach.JMIR medical informatics · 2026Article
- Deciphering shared molecular dysregulation across Parkinson's disease variants using a multi-modal network-based data integration and analysis.NPJ Parkinson's disease · 2025Article
- Introducing Attribute Association Graphs to Facilitate Medical Data Exploration: Development and Evaluation Using Epidemiological Study Data.JMIR medical informatics · 2024Article
- Article
- Rewiring Drug Research and Development through Human Data-Driven Discovery (HDPharmaceutics · 2023Review
- A guide for developing comprehensive systems biology maps of disease mechanisms: planning, construction and maintenance.Frontiers in bioinformatics · 2023Article
- Applying the FAIR principles to data in a hospital: challenges and opportunities in a pandemic.Journal of biomedical semantics · 2022Article
- Knowledge graph analytics platform with LINCS and IDG for Parkinson's disease target illumination.BMC bioinformatics · 2022Article
- Overview of methods for characterization and visualization of a protein-protein interaction network in a multi-omics integration context.Frontiers in molecular biosciences · 2022Review
- TAGOPSIN: collating taxa-specific gene and protein functional and structural information.BMC bioinformatics · 2021Article
- LinkedImm: a linked data graph database for integrating immunological data.BMC bioinformatics · 2021Article
- An overview of graph databases and their applications in the biomedical domain.Database : the journal of biological databases and curation · 2021Article
- BioDWH2: an automated graph-based data warehouse and mapping tool.Journal of integrative bioinformatics · 2021Article
- Multi-omics integration in biomedical research - A metabolomics-centric review.Analytica chimica acta · 2021Review
- Network graph representation of COVID-19 scientific publications to aid knowledge discovery.BMJ health & care informatics · 2021Article
- Expert-Augmented Computational Drug Repurposing Identified Baricitinib as a Treatment for COVID-19.Frontiers in pharmacology · 2021Article
- Applying graph database technology for analyzing perturbed co-expression networks in cancer.Database : the journal of biological databases and curation · 2020Article
- Exploring Integrative Analysis Using the BioMedical Evidence Graph.JCO clinical cancer informatics · 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
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSystems biology experiments generate large volumes of data of multiple modalities and this information presents a challenge for integration due to a mix of complexity together with rich semantics. Here, we describe how graph databases provide a powerful framework for storage, querying and envisioning of biological data.
resultsWe show how graph databases are well suited for the representation of biological information, which is typically highly connected, semi-structured and unpredictable. We outline an application case that uses the Neo4j graph database for building and querying a prototype network to provide biological context to asthma related genes.
conclusionsOur study suggests that graph databases provide a flexible solution for the integration of multiple types of biological data and facilitate exploratory data mining to support hypothesis generation.
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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.