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Predicting translational progress in biomedical research.

ABSTRACT: Fundamental scientific advances can take decades to translate into improvements in human health. Shortening this interval would increase the rate at which scientific discoveries lead to successful treatment of human disease. One way to accomplish this would be to identify which advances in knowledge are most likely to translate into clinical research. Toward that end, we built a machine learning system that detects whether a paper is likely to be cited by a future clinical trial or guideline. Despite the noisiness of citation dynamics, as little as 2 years of postpublication data yield accurate predictions about a paper's eventual citation by a clinical article (accuracy = 84%, F1 score = 0.56; compared to 19% accuracy by chance). We found that distinct knowledge flow trajectories are linked to papers that either succeed or fail to influence clinical research. Translational progress in biomedicine can therefore be assessed and predicted in real time based on information conveyed by the scientific community's early reaction to a paper.

SUBMITTER: Hutchins BI

PROVIDER: S-EPMC6786525 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

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Predicting translational progress in biomedical research.

Hutchins B Ian BI Davis Matthew T MT Meseroll Rebecca A RA Santangelo George M GM

PLoS biology 20191010 10

Fundamental scientific advances can take decades to translate into improvements in human health. Shortening this interval would increase the rate at which scientific discoveries lead to successful treatment of human disease. One way to accomplish this would be to identify which advances in knowledge are most likely to translate into clinical research. Toward that end, we built a machine learning system that detects whether a paper is likely to be cited by a future clinical trial or guideline. De ...[more]

PMID: 31600189

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Project description:BackgroundThe evaluation of translational health research is important for various reasons such as the research impact assessment, research funding allocation, accountability, and strategic research policy formulation. The purpose of this study was to evaluate the research productivity, strength and diversity of research collaboration networks and impact of research supported by a large biomedical research centre in the United Kingdom (UK).MethodsBibliometric analysis of research publications by translational researchers affiliated with the National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (BRC) from April 2012 to March 2017.ResultsAnalysis included 2377 translational research publications that were published during the second 5-year funding period of the NIHR Oxford BRC. Author details were available for 99.75% of the publications with DOIs (2359 of 2365 with DOIs), and the number of authors per publication was median 9 (mean = 18.03, SD = 3.63, maximum = 2467 authors). Author lists also contained many consortia, groups, committees, and teams (n = 165 in total), with 1238 additional contributors, where membership was reported. The BRC co-authorship i.e., research collaboration network for these publications involved 20,229 nodes (authors, of which 1606 nodes had Oxford affiliations), and approximately 4.3 million edges (authorship linkages). Articles with a valid DOIs (2365 of 2377, 99.5%) were collectively cited more than 155,000 times and the average Field Citation Ratio was median 6.75 (geometric mean = 7.12) while the average Relative Citation Ratio was median 1.50 (geometric mean = 1.83) for the analysed publications.ConclusionsThe NIHR Oxford BRC generated substantial translational research publications and facilitated a huge collaborative network of translational researchers working in complex structures and consortia, which shows success across the whole of this BRC funding period. Further research involving continued uptake of unique persistent identifiers and the tracking of other research outputs such as clinical innovations and patents would allow a more detailed understanding of large research enterprises such as NIHR BRCs in the UK.

Dataset Information

Predicting translational progress in biomedical research.

Publications

Predicting translational progress in biomedical research.

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