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

Duck, G., Nenadic, G., Brass, A., Robertson, D. L. and Stevens, R. (2013) bioNerDS: exploring bioinformatics' database and software use through literature mining. BMC Bioinformatics, 14, 194. (doi:10.1186/1471-2105-14-194) (PMID:23768135) (PMCID:PMC3693927)

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Abstract

Background: Biology-focused databases and software define bioinformatics and their use is central to computational biology. In such a complex and dynamic field, it is of interest to understand what resources are available, which are used, how much they are used, and for what they are used. While scholarly literature surveys can provide some insights, large-scale computer-based approaches to identify mentions of bioinformatics databases and software from primary literature would automate systematic cataloguing, facilitate the monitoring of usage, and provide the foundations for the recovery of computational methods for analysing biological data, with the long-term aim of identifying best/common practice in different areas of biology. Results: We have developed bioNerDS, a named entity recogniser for the recovery of bioinformatics databases and software from primary literature. We identify such entities with an F-measure ranging from 63% to 91% at the mention level and 63-78% at the document level, depending on corpus. Not attaining a higher F-measure is mostly due to high ambiguity in resource naming, which is compounded by the on-going introduction of new resources. To demonstrate the software, we applied bioNerDS to full-text articles from BMC Bioinformatics and Genome Biology. General mention patterns reflect the remit of these journals, highlighting BMC Bioinformatics's emphasis on new tools and Genome Biology's greater emphasis on data analysis. The data also illustrates some shifts in resource usage: for example, the past decade has seen R and the Gene Ontology join BLAST and GenBank as the main components in bioinformatics processing. Conclusions: We demonstrate the feasibility of automatically identifying resource names on a large-scale from the scientific literature and show that the generated data can be used for exploration of bioinformatics database and software usage. For example, our results help to investigate the rate of change in resource usage and corroborate the suspicion that a vast majority of resources are created, but rarely (if ever) used thereafter. bioNerDS is available at http://bionerds.sourceforge.net/.

Item Type:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Robertson, Professor David
Authors: Duck, G., Nenadic, G., Brass, A., Robertson, D. L., and Stevens, R.
College/School:College of Medical Veterinary and Life Sciences > Institute of Infection Immunity and Inflammation
Journal Name:BMC Bioinformatics
Publisher:BioMed Central
ISSN:1471-2105
ISSN (Online):1471-2105
Copyright Holders:Copyright © 2013 Duck et al.
First Published:First published in BMC Bioinformatics 14:194
Publisher Policy:Reproduced under a Creative Commons License

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