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DancePartner: Python Package to Mine Multiomics Relationship Networks from Literature and Databases.

David J Degnan, Clayton W Strauch, Moses Y Obiri

  • 1Grand Valley State University, 1 Campus Drive, Allendale, Michigan 49401, United States.

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|November 4, 2025
PubMed
Summary

The DancePartner Python package facilitates multiomics network construction by extracting biomolecule relationships from scientific literature and databases, aiding research in understudied species.

Keywords:
BERTbiological networksdatabase miningliterature miningpythonrelationship extraction

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Area of Science:

  • Systems Biology
  • Bioinformatics
  • Computational Biology

Background:

  • Multiomics experiments aim to elucidate molecular biology alterations between conditions.
  • Understanding biomolecule relationships (e.g., interactions, metabolic pathways) is crucial.
  • Existing databases lack comprehensive relationship data, especially for understudied species, necessitating literature mining.

Purpose of the Study:

  • To develop an accessible tool for extracting biomolecule relationships from literature and databases.
  • To create a Python package, DancePartner, for automated relationship extraction and multiomics network construction.
  • To enable mapping of biomolecule synonyms to standardized identifiers and visualization of networks.

Main Methods:

  • Developed the DancePartner Python package for literature and database mining.
  • Implemented functions for biomolecule synonym mapping and network visualization.
  • Utilized large datasets of papers and abstracts for *Caenorhabditis elegans* and *Saccharomyces cerevisiae*.

Main Results:

  • Successfully extracted and integrated biomolecule relationships from literature and public databases (KEGG, WikiPathways, UniProt, LipidMaps).
  • Demonstrated DancePartner's capability using extensive datasets for *C. elegans* and *S. cerevisiae*.
  • Visualized and characterized the resulting multiomics networks, comparing network build times.

Conclusions:

  • DancePartner provides an accessible solution for constructing multiomics networks, particularly for species with limited existing data.
  • The package facilitates the integration of diverse data sources for comprehensive biological network analysis.
  • Automated literature mining and network visualization are key for advancing multiomics research.