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Related Experiment Video

Updated: Oct 30, 2025

Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification

Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification

Published on: November 15, 2017

Mining Proteome Research Reports: A Bird's Eye View.

Jagajjit Sahu¹

¹National Centre for Cell Science (NCCS), NCCS Complex, Pune University Campus, Ganeshkhind Road, Pune 411007, Maharashtra, India.

|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.

Keywords:

NLP bio-concepts gene–gene network proteome scientometrics text mining

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Related Experiment Videos

Last Updated: Oct 30, 2025

Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification

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Published on: November 15, 2017

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Published on: June 14, 2017

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Published on: May 22, 2018

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.