Affiliation:
1. Department of Psychiatry , University of Illinois at Chicago , Chicago , IL 60612 , USA
Abstract
Abstract
Purpose
The late Don R. Swanson was well appreciated during his lifetime as Dean of the Graduate Library School at University of Chicago, as winner of the American Society for Information Science Award of Merit for 2000, and as author of many seminal articles. In this informal essay, I will give my personal perspective on Don’s contributions to science, and outline some current and future directions in literature-based discovery that are rooted in concepts that he developed.
Design/methodology/approach
Personal recollections and literature review.
Findings
The Swanson A-B-C model of literature-based discovery has been successfully used by laboratory investigators analyzing their findings and hypotheses. It continues to be a fertile area of research in a wide range of application areas including text mining, drug repurposing, studies of scientific innovation, knowledge discovery in databases, and bioinformatics. Recently, additional modes of discovery that do not follow the A-B-C model have also been proposed and explored (e.g. so-called storytelling, gaps, analogies, link prediction, negative consensus, outliers, and revival of neglected or discarded research questions).
Research limitations
This paper reflects the opinions of the author and is not a comprehensive nor technically based review of literature-based discovery.
Practical implications
The general scientific public is still not aware of the availability of tools for literature-based discovery. Our Arrowsmith project site maintains a suite of discovery tools that are free and open to the public (http://arrowsmith.psych.uic.edu), as does BITOLA which is maintained by Dmitar Hristovski (http://http://ibmi.mf.uni-lj.si/bitola), and Epiphanet which is maintained by Trevor Cohen (http://epiphanet.uth.tmc.edu/). Bringing user-friendly tools to the public should be a high priority, since even more than advancing basic research in informatics, it is vital that we ensure that scientists actually use discovery tools and that these are actually able to help them make experimental discoveries in the lab and in the clinic.
Originality/value
This paper discusses problems and issues which were inherent in Don’s thoughts during his life, including those which have not yet been fully taken up and studied systematically.
Reference79 articles.
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2. Baek, S.H., Lee, D., Kim, M., Lee, J.H., & Song, M. (2017). Enriching plausible new hypothesis generation in PubMed. PLoS ONE, 12(7), e0180539.28678852
3. Bekhuis, T. (2006). Conceptual biology, hypothesis discovery, and text mining: Swanson’s legacy. Biomedical Digital Libraries, 3:2. Retrieved on August 9, 2017, from https://bio-diglib.biomedcentral.com/articles/10.1186/1742-5581-3-2.
4. Bruza, P., & Weeber, M. (Eds.) (2008). Literature-based discovery. Berlin: Springer-Verlag.
5. Cairelli, M.J., Miller, C.M., Fiszman, M., Workman, T.E., & Rindflesch, T.C. (2013). Semantic MEDLINE for discovery browsing: Using semantic predications and the literature-based discovery paradigm to elucidate a mechanism for the obesity paradox. In AMIA Annual Symposium Proceedings (pp. 164–173). Retrieved on August 9, 2017, from http://europepmc.org/articles/PMC3900170.
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