ArticleGenomics & informatics2017
Use of Graph Database for the Integration of Heterogeneous Biological Data.
Article in Genomics & informatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 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
28 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
- Integrating Clinical and Systemic Knowledge in Periodontal and Peri-Implant Disease Education Through a Knowledge Graph.Journal of dental education · 2026Article
- Article
- Comprehensive applications of the artificial intelligence technology in new drug research and development.Health information science and systems · 2024Review
- GenomicKB: a knowledge graph for the human genome.Nucleic acids research · 2023Article
- Development of a Knowledge Graph Embeddings Model for Pain.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2023Article
- Development of a knowledge graph framework to ease and empower translational approaches in plant research: a use-case on grain legumes.Frontiers in artificial intelligence · 2023Article
- Graph4Med: a web application and a graph database for visualizing and analyzing medical databases.BMC bioinformatics · 2022Article
- FHIR-Ontop-OMOP: Building clinical knowledge graphs in FHIR RDF with the OMOP Common data Model.Journal of biomedical informatics · 2022Article
- A knowledge graph to interpret clinical proteomics data.Nature biotechnology · 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
- The importance of residue-level filtering and the Top2018 best-parts dataset of high-quality protein residues.Protein science : a publication of the Protein Society · 2022Article
- Scientific evidence based rare disease research discovery with research funding data in knowledge graph.Orphanet journal of rare diseases · 2021Article
- 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
- KnetMiner: a comprehensive approach for supporting evidence-based gene discovery and complex trait analysis across species.Plant biotechnology journal · 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
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.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Understanding complex relationships among heterogeneous biological data is one of the fundamental goals in biology. In most cases, diverse biological data are stored in relational databases, such as MySQL and Oracle, which store data in multiple tables and then infer relationships by multiple-join statements. Recently, a new type of database, called the graph-based database, was developed to natively represent various kinds of complex relationships, and it is widely used among computer science communities and IT industries. Here, we demonstrate the feasibility of using a graph-based database for complex biological relationships by comparing the performance between MySQL and Neo4j, one of the most widely used graph databases. We collected various biological data (protein-protein interaction, drug-target, gene-disease, etc.) from several existing sources, removed duplicate and redundant data, and finally constructed a graph database containing 114,550 nodes and 82,674,321 relationships. When we tested the query execution performance of MySQL versus Neo4j, we found that Neo4j outperformed MySQL in all cases. While Neo4j exhibited a very fast response for various queries, MySQL exhibited latent or unfinished responses for complex queries with multiple-join statements. These results show that using graph-based databases, such as Neo4j, is an efficient way to store complex biological relationships. Moreover, querying a graph database in diverse ways has the potential to reveal novel relationships among heterogeneous biological data.
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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.