Abstract
Abstract Background Much scientific knowledge is contained in the details of the full-text biomedical literature. Most research in automated retrieval presupposes that the target literature can be downloaded and preprocessed prior to query. Unfortunately, this is not a practical or maintainable option for most users due to licensing restrictions, website terms of use, and sheer volume. Scientific article full-text is increasingly queriable through portals such as PubMed Central, Highwire Press, Scirus, and Google Scholar. However, because these portals only support very basic Boolean queries and full text is so expressive, formulating an effective query is a difficult task for users. We propose improving the formulation of full-text queries by using the open access literature as a proxy for the literature to be searched. We evaluated the feasibility of this approach by building a high-precision query for identifying studies that perform gene expression microarray experiments. *Methodology and Results* We built decision rules from unigram and bigram features of the open access literature. Minor syntax modifications were needed to translate the decision rules into the query languages of PubMed Central, Highwire Press, and Google Scholar. We mapped all retrieval results to PubMed identifiers and considered our query results as the union of retrieved articles across all portals. Compared to our reference standard, the derived full-text query found 56% (95% confidence interval, 52% to 61%) of intended studies, and 90% (86% to 93%) of studies identified by the full-text search met the reference standard criteria. Due to this relatively high precision, the derived query was better suited to the intended application than alternative baseline MeSH queries. *Significance* Using open access literature to develop queries for full-text portals is an open, flexible, and effective method for retrieval of biomedical literature articles based on article full-text. We hope our approach will raise awareness of the constraints and opportunities in mainstream full-text information retrieval and provide a useful tool for today’s researchers.
Highlights
Much scientific knowledge is contained in the details of the full-text biomedical literature
Compared to our reference standard, the derived fulltext query found 56% (95% confidence interval, 52% to 61%) of intended studies, and 90% (86% to 93%) of studies identified by the full-text search met the reference standard criteria
Due to this relatively high precision, the derived query was better suited to the intended application than alternative baseline Medical Subject Heading (MeSH) queries
Summary
Much scientific knowledge is contained in the details of the full-text biomedical literature. Most research in automated retrieval presupposes that the target literature can be downloaded and preprocessed prior to query. This is not a practical or maintainable option for most users due to licensing restrictions, website terms of use, and sheer volume. Scientific article full-text is increasingly queriable through portals such as PubMed Central, Highwire Press, Scirus, and Google Scholar. Because these portals only support very basic Boolean queries and full text is so expressive, formulating an effective query is a difficult task for users. We evaluated the feasibility of this approach by building a high-precision query for identifying studies that perform gene expression microarray experiments
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