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Updated: May 25, 2026

Comparing Bibliometric Analysis Using PubMed, Scopus, and Web of Science Databases

Comparing Bibliometric Analysis Using PubMed, Scopus, and Web of Science Databases

Published on: October 24, 2019

Connecting the dots between PubMed abstracts.

M Shahriar Hossain¹, Joseph Gresock, Yvette Edmonds

¹Department of Computer Science, Virginia Tech, Blacksburg, Virginia, United States of America. msh@vt.edu

|January 12, 2012

Summary

This summary is machine-generated.

This study introduces a storytelling algorithm to connect fragmented biological research across publications. It helps scientists discover new connections and generate hypotheses from vast amounts of literature.

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Area of Science:

Bioinformatics
Computational Biology
Systems Biology

Background:

Vast amounts of biological research are published across diverse journals.
Integrating information from multiple publications is crucial for understanding complex biological systems.
Unraveling relationships between extracellular inputs and molecular responses requires synthesizing diverse findings.

Purpose of the Study:

To develop an automated approach for biological knowledge discovery from PubMed abstracts.
To create a "storytelling" algorithm for connecting disparate scientific literature.
To facilitate hypothesis generation and exploration of connections within large biological knowledge bases.

Main Methods:

An automated storytelling algorithm was developed to identify chains of publications linking a start and end abstract.
The algorithm identifies intermediate publications with significant content similarity.
Coherence and overlap filters were designed as post-processing steps to ensure story integrity.

Main Results:

The algorithm successfully generated "stories" connecting publications with little initial overlap.
Case studies demonstrated applications in many-one, many-many, and one-to-one relationship discoveries.
The pipeline significantly narrowed down relevant documents from hundreds of thousands to approximately one hundred stories.

Conclusions:

The storytelling algorithm serves as a valuable discovery aid for hypothesis generation.
It enables efficient exploration of connections within large, unstructured biological knowledge bases.
This approach aids scientists in focusing their research by connecting disparate findings.