Open AccessA Novel Tool for Translational Research Discovery
Author(s) -
Fontelo Paul,
Liu Fang
Publication year - 2009
Publication title -
clinical and translational science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.303
H-Index - 44
eISSN - 1752-8062
pISSN - 1752-8054
DOI - 10.1111/j.1752-8062.2009.00139.x
Subject(s) - translational research , translational science , translational medicine , medline , computer science , data science , psychological intervention , medicine , political science , pathology , psychiatry , law
T he Institute of Medicine’s Clinical Research Roundtable highlighted two translational blocks that prevent discoveries of promising drugs, diagnostic technologies, or therapeutic procedures that are generated by basic research from getting translated into clinical applications.1 Success is not the norm, even for research with the most promising results.2 For basic research that ultimately leads to patient applications, the interval is long, oft en more than 10 years.2 Several explanations for the lag were proposed: funding sustainability through the tedious discovery to application process, arduous challenges of validating and ensuring that the product is safe and eff ective, competing research priorities, and a lack of trained translational investigators and participants to carry on research.1,2 Another explanation could simply be a failure to discover novel research. Th ere are now more than 18 million citations in MEDLINE/PubMed from a subset of about 5,200 select journals. Th e total number of science journals that may contain potential discoveries is manyfold more. Th e National Library of Medicine (NLM) at the National Institutes of Health has undertaken a project to facilitate the discovery of published translational science research cited in MEDLINE/PubMed. Th is project is based on the underlying hypothesis that creating a set of fi lters and limiters, specifi cally selected to fi nd translational research publications, may be useful in fi nding promising past and recent novel studies on interventions for specifi c disease processes. Th e project development began by creating a database of more than 6,800 term combinations (eg, “future therapy,” “novel application,” and “potential therapeutic target”) using vocabulary from known translational articles. Th ese terms were used to search PubMed using E-Utilities. Th e database was continuously modifi ed to optimize time needed to search and maximize yield of translational publications retrieved. Th e term combinations were reorganized to a current fi nal list of about 50 search limiters derived through repeated searching and ranking of terms.