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Using a corpus consisting of ~20,000 articles from the PubMed Central (PMC) open-access subset and Directory of Open Access Journals (DOAJ), it was found that many explicit protein–protein interactions only are mentioned in the full text. While text-mining results from accessible full-text articles have already become an integral part of some databases (reviewed recently for protein-protein interactions ), very few studies to date have compared text mining of abstracts and full-text articles. The latter is often considered to be more speculative compared to the abstract. Moreover, they present existing and generally accepted knowledge in the introduction (often presented in the context of summaries of the findings), and move on to reporting more in-depth results, while discussion sections put the results in perspective and mention limitations and concerns. By comparison, full-text articles contain complex tables, display items and references. Abstracts are comprised of shorter sentences and very succinct text presenting only the most important findings. Nevertheless, to date no studies have presented a systematic comparison of the performance comparing a very large number of abstracts and full-texts in corpora that are similar in size to MEDLINE.įull-text articles and abstracts are structurally different. However, full-text articles are becoming more accessible and there is a growing interest in text mining of complete articles. The main text source for scientific literature has been the MEDLINE corpus of abstracts, essentially due to the restricted availability of full-text articles. Thus, text mining has become an integral part of many resources serving a wide audience of scientists.
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In addition, text mining is routinely used to support manual curation of biological databases. Furthermore, the extracted information has been used as annotation of specialized databases and tools (reviewed in ).
Biomedical text mining is concerned with the extraction of information regarding biological entities, such as genes and proteins, phenotypes, or even more broadly biological pathways (reviewed extensively in ) from sources like scientific literature, electronic patient records, and most recently patents.
Text mining is used to extract facts and relationships in a structured form that can be used to annotate specialized databases, to transfer knowledge between domains and more generally within business intelligence to support operational and strategic decision-making. Text mining has become a widespread approach to identify and extract information from unstructured text. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Ĭompeting interests: SB and LJJ are on the scientific advisory board and have been among the founders of Intomics A/S with equity in the company. įunding: This work was funded by a grant from the Danish e-Infrastructure Cooperation (ActionableBiomarkersDK, (SB), and by the Novo Nordisk Foundation (grant agreement NNF14CC0001, ) (SB, LJJ).
The entities mentioned in articles used for benchmarking can be found at. The Z-scores used for benchmarking can be found at. The DOIs for the articles can be found at. The dictionaries used for named entity recognition can be found at.
This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.ĭata Availability: Due to copyright and legal agreements the full text articles cannot be made available. Received: JAccepted: JanuPublished: February 15, 2018Ĭopyright: © 2018 Westergaard et al. PLoS Comput Biol 14(2):Įditor: Andrey Rzhetsky, University of Chicago, UNITED STATES Citation: Westergaard D, Stærfeldt H-H, Tønsberg C, Jensen LJ, Brunak S (2018) A comprehensive and quantitative comparison of text-mining in 15 million full-text articles versus their corresponding abstracts.
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