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Updated: Oct 30, 2025
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Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification
Published on: November 15, 2017
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Mining Proteome Research Reports: A Bird's Eye View
1National Centre for Cell Science (NCCS), NCCS Complex, Pune University Campus, Ganeshkhind Road, Pune 411007, Maharashtra, India.
Proteomes
|July 2, 2021
Summary
This study used text mining to analyze PubMed proteome literature, revealing publication trends and creating gene co-occurrence networks. The gene p53 was most central in the proteome network analysis.
Area of Science:
- Bioinformatics
- Computational Biology
- Scientific Literature Analysis
Background:
- The increasing volume of scientific data presents significant data management challenges for researchers.
- Modern analytical tools and programming languages enable better access to scientific literature and domain-specific knowledge.
Purpose of the Study:
- To systematically analyze PubMed proteome literature using text mining.
- To extract publication trends, frequent keywords, bioconcepts, and construct gene-gene co-occurrence networks.
Main Methods:
- Scientometric analysis and information extraction from PubMed.
- Utilized PubTator for bioconcept extraction and network construction.
- Analyzed 24,350 articles under 28 Medical Subject Headings (MeSH).
Main Results:
- Identified publication trends and frequent keywords within proteome literature.
- Extracted 322,026 bioconcepts across 10 classes.
- Constructed gene-gene co-occurrence networks, highlighting mTOR, AKT, and p53 as key nodes.
Conclusions:
- Text mining and bibliometrics provide a comprehensive analysis of proteome literature.
- Gene co-occurrence networks reveal significant biological associations and central genes like p53.
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