PubRunner

Biomedical text mining promises to assist biologists in quickly navigating the
combined knowledge in their domain. This would allow improved understanding of
the complex interactions within biological systems and faster hypothesis
generation. New biomedical research articles are published daily and text mining
tools are only as good as the corpus from which they work. Many text mining tools
are underused because their results are static and do not reflect the constantly
expanding knowledge in the field. In order for biomedical text mining to become
an indispensable tool used by researchers, this problem must be addressed. To
this end, we present PubRunner, a framework for regularly running text mining
tools on the latest publications. PubRunner is lightweight, simple to use, and
can be integrated with an existing text mining tool. The workflow involves
downloading the latest abstracts from PubMed, executing a user-defined tool,
pushing the resulting data to a public FTP or Zenodo dataset, and publicizing the
location of these results on the public PubRunner website. We illustrate the use
of this tool by re-running the commonly used word2vec tool on the latest PubMed
abstracts to generate up-to-date word vector representations for the biomedical
domain. This shows a proof of concept that we hope will encourage text mining
developers to build tools that truly will aid biologists in exploring the latest
publications.

PubRunner: A light-weight framework for updating text mining results.

PubRunner is a workflow to mine and download <a href="https://pubmed.ncbi.nlm.nih.gov/" target="_blank" rel="noopener">PubMed</a> abstracts, execute a text-mining tool of choice, and upload them to an FTP server or <a href="https://zenodo.org/" target="_blank" rel="noopener">Zenodo</a> dataset.

PubRunner

Topic

Detail

Publications

Download and documentation