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bioNerDS: exploring bioinformatics' database and software use through literature mining.

Geraint Duck¹, Goran Nenadic, Andy Brass

¹School of Computer Science, The University of Manchester, Manchester, UK.

BMC Bioinformatics

|June 18, 2013

Summary

This summary is machine-generated.

We developed bioNerDS to automatically identify bioinformatics databases and software from scientific literature. This tool tracks resource usage, revealing trends like the rise of R and Gene Ontology in bioinformatics.

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Area of Science:

Computational biology
Bioinformatics
Scientific literature mining

Background:

Bioinformatics databases and software are crucial for computational biology.
Understanding the usage patterns of these resources is essential in a dynamic field.
Automated methods for cataloging and monitoring resource use are needed.

Purpose of the Study:

To develop a named entity recognizer (bioNerDS) for identifying bioinformatics databases and software in scientific literature.
To automate the cataloging and monitoring of bioinformatics resource usage.
To provide a foundation for analyzing computational methods and identifying best practices.

Main Methods:

Developed bioNerDS, a named entity recognizer.
Applied bioNerDS to full-text articles from BMC Bioinformatics and Genome Biology.
Analyzed mention patterns and usage trends of bioinformatics resources.

Main Results:

Achieved F-measures of 63-91% at the mention level and 63-78% at the document level.
Identified high ambiguity in resource naming and the introduction of new resources as challenges.
Observed shifts in resource usage, with R and Gene Ontology becoming prominent alongside BLAST and GenBank.

Conclusions:

Demonstrated the feasibility of large-scale, automated identification of bioinformatics resource names from literature.
Showcased the utility of generated data for exploring bioinformatics database and software usage.
Highlighted the rapid evolution of resource usage and the potential for many created resources to be underutilized.