Zobraziť v EDS

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

Uložené v:
Podrobná bibliografia
Názov: DancePartner: Python Package to Mine Multiomics Relationship Networks from Literature and Databases.
Autori: Degnan DJ, Strauch CW, Obiri MY, VonKaenel ED, Adrian DW; Grand Valley State University, 1 Campus Drive, Allendale, Michigan 49401, United States., Bramer LM
Zdroj: Journal of proteome research [J Proteome Res] 2025 Dec 05; Vol. 24 (12), pp. 6305-6310. Date of Electronic Publication: 2025 Nov 04.
Spôsob vydávania: Journal Article
Jazyk: English
Informácie o časopise: Publisher: American Chemical Society Country of Publication: United States NLM ID: 101128775 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1535-3907 (Electronic) Linking ISSN: 15353893 NLM ISO Abbreviation: J Proteome Res Subsets: MEDLINE
Imprint Name(s): Original Publication: Washington, D.C. : American Chemical Society, c2002-
Výrazy zo slovníka MeSH: Software* , Data Mining*/methods , Proteomics*/methods, Animals ; Saccharomyces cerevisiae/metabolism ; Saccharomyces cerevisiae/genetics ; Caenorhabditis elegans/metabolism ; Databases, Factual ; Multiomics
Abstrakt: A goal of multiomics experiments is to understand how mechanistic molecular biology is altered between conditions, typically a control group and experimental groups. Oftentimes, this involves studying changes in biomolecule relationships (e.g., interactions and metabolic relationships) of several types of biomolecules (e.g., lipids, metabolites, gene products like proteins). Though several databases contain relationships between biomolecules, understudied species may have little to no relationship information in databases and thus must be mined from the literature. There are several challenges to literature mining, including automated full-text extraction, duplicate biomolecule term collapsing, and implementation of complex machine learning tools. To make relationship extraction more accessible to the community, a Python package called DancePartner was developed to allow for the extraction of relationships from literature and databases, with functions to map biomolecule synonyms to standardized identifiers and visualize and characterize the resulting multiomics network. Here, two example data sets are provided to demonstrate the capabilities of DancePartner : one of 14,986 papers and abstracts for Caernohabditis elegans , and another of 33,606 papers and abstracts for Saccharomyces cerevisiae . These relationships are combined with relationships from KEGG, WikiPathways, UniProt, and LipidMaps, and they are visualized. Networks are then compared for their differences in build times.
Contributed Indexing: Keywords: BERT; biological networks; database mining; literature mining; python; relationship extraction
Entry Date(s): Date Created: 20251104 Date Completed: 20251205 Latest Revision: 20251205
Update Code: 20251205
DOI: 10.1021/acs.jproteome.5c00520
PMID: 41186395
Databáza: MEDLINE
Popis
Abstrakt:A goal of multiomics experiments is to understand how mechanistic molecular biology is altered between conditions, typically a control group and experimental groups. Oftentimes, this involves studying changes in biomolecule relationships (e.g., interactions and metabolic relationships) of several types of biomolecules (e.g., lipids, metabolites, gene products like proteins). Though several databases contain relationships between biomolecules, understudied species may have little to no relationship information in databases and thus must be mined from the literature. There are several challenges to literature mining, including automated full-text extraction, duplicate biomolecule term collapsing, and implementation of complex machine learning tools. To make relationship extraction more accessible to the community, a Python package called DancePartner was developed to allow for the extraction of relationships from literature and databases, with functions to map biomolecule synonyms to standardized identifiers and visualize and characterize the resulting multiomics network. Here, two example data sets are provided to demonstrate the capabilities of DancePartner : one of 14,986 papers and abstracts for Caernohabditis elegans , and another of 33,606 papers and abstracts for Saccharomyces cerevisiae . These relationships are combined with relationships from KEGG, WikiPathways, UniProt, and LipidMaps, and they are visualized. Networks are then compared for their differences in build times.
ISSN:1535-3907
DOI:10.1021/acs.jproteome.5c00520