ArticleBioinformatics (Oxford, England)2026
pyBiodatafuse: extending interoperability of data using modular queries across biomedical resources.
Article in Bioinformatics (Oxford, England), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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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
1 citing paper in PubMed.
- pyBiodatafuse: extending interoperability of data using modular queries across biomedical resources.Bioinformatics (Oxford, England) · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
motivationIntegrating omics data analysis with publicly available databases is crucial for unravelling complex biological mechanisms. However, this integration process is often intricate and time-consuming due to the diversity and complexity of the data involved. Achieving consistent harmonization across data types is challenging when managing disparate formats and sources. To address these issues, we introduce pyBiodatafuse, a query-based Python tool designed to integrate biomedical databases. This tool establishes a modular framework that simplifies data wrangling, enabling the creation of context-specific knowledge graphs (KGs) while supporting graph-based analyses.
resultsWe developed a pipeline for generating context-specific knowledge graphs dynamically, allowing users to create KGs on the fly from a set of gene or metabolite identifiers. pyBiodatafuse features a user-friendly interface that streamlines this process, making it accessible even to researchers without extensive computational expertise. Additionally, the tool offers plugins for widely used platforms such as Cytoscape, Neo4j, and GraphDB, enabling local hosting of resulting property and RDF graphs. This versatility ensures that generated KGs can be efficiently utilized within diverse research workflows. To demonstrate its potential, we used pyBiodatafuse to create a graph for post-COVID syndrome using differential gene expression data, showcasing its ability to build adaptable and context-specific knowledge representations. Thus, pyBiodatafuse sets the stage for streamlined data integration, empowering researchers to focus on discovery and analysis without being hindered by data management complexities. AVAILABILITY AND IMPLEMENTATION: pyBiodatafuse is open-source, with its source code and PyPi package available at https://github.com/BioDataFuse/pyBiodatafuse and https://pypi.org/project/pyBiodatafuse/. The user interface can be accessed at https://biodatafuse.org/. Additionally, a release has been made on Zenodo at https://doi.org/10.5281/zenodo.18468942.
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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.