Distribution of information in biomedical abstracts and full-text publications

Schuemie, Martijn J.; Weeber, Marc; Schijvenaars, Bob J. A.; Mulligen, Erik M. van; Eijk, C. Christiaan van der; Jelier, Rob; Mons, Barend; Kors, Jan A.

doi:10.1093/bioinformatics/bth291

Cited by 89 publications

(61 citation statements)

References 4 publications

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“…As well, B-terms that refer to the same thing (spelling variants, synonyms or abbreviations and their corresponding long forms) should ideally be merged and considered together. It may also be worthwhile to take B-terms not only from the titles of papers, but from their abstracts as well, because the abstract conveys a significant portion of the total information contained in a scientific paper (Kostoff et al, 2004;Schuemie et al, 2004;Shah et al, 2003).…”

Section: Discussionmentioning

confidence: 99%

A quantitative model for linking two disparate sets of articles in MEDLINE

Torvik

Smalheiser

2007

Bioinformatics

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Background: Identifying information that implicitly links two disparate sets of articles is a fundamental and intuitive data mining strategy that can help investigators address real scientific questions. The Arrowsmith two-node search finds title words and phrases (so-called B-terms) that are shared across two sets of articles within MEDLINE and displays them in a manner that facilitates human assessment. A serious stumbling-block has been the lack of a quantitative model for predicting which of the hundreds if not thousands of B-terms computed for a given search are most likely to be relevant to the investigator. Methodology/Principal Findings: Using a public two-node search interface, field testers devised a set of two-node searches under real life conditions and a certain number of B-terms were marked relevant. These were employed as 'gold standards;' each B-term was characterized according to eight complementary features that were strongly correlated with relevance. A logistic regression model was developed that permits one to estimate the probability of relevance for each B-term, to rank B-terms according to their likely relevance, and to estimate the overall number of relevant B-terms inherent in a given two-node search. Conclusions/Significance: The model greatly simplifies and streamlines the process of carrying out a two-node search, and may be applicable to a number of other literature-based discovery applications, including the so-called one-node search and related gene-centric strategies that incorporate implicit links to predict how genes may be related to each other and to human diseases. This should encourage much wider exploration of text mining for implicit information among the general scientific community. Availability: Two-node searches can be carried out freely at http:// arrowsmith

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Section: Discussionmentioning

confidence: 99%

A quantitative model for linking two disparate sets of articles in MEDLINE

Torvik

Smalheiser

2007

Bioinformatics

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“…i5K Insect and other Arthropod Genome Sequencing Initiative, a project to sequence the genomes of 5000 insect species in the next 5 years. 17 Statistics derived from an analysis of all articles published in 90 journals over 3 years (Schuemie et al, 2004). more than one chromosome (e.g.…”

Section: Discussionmentioning

confidence: 99%

Gene symbol precision

Bennani‐Baïti

Bennani-Baı̈ti

2012

Gene

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“…Several previous studies have emphasized that specific terms or MeSH concepts may be enriched in particular sections of scientific papers [6,7]. We examined how the set of characteristic terms are distributed among 8 different sections of papers encoded in MEDLINE fields for each of 10 disciplinary journals, the California literature and the random literature: text (comprising title and abstract fields); ti (title); ab (abstract); lastsen (last sentence of the abstract); ti + ab (present both in the title and in the abstract of at least one paper in the literature, though not necessarily the same paper); tiab (in the title and the abstract of the same paper, for at least one paper in the literature); ti + lastsen (in title and last sentence of the abstract, for at least one paper in the literature, though not necessarily the same paper); and tiab + lastsen (in tiab and in last sentence of the abstract for at least one paper in the literature).…”

Section: Distribution Of Characteristic Terms Within Individual Articmentioning

confidence: 99%

Distribution of “Characteristic” Terms in MEDLINE Literatures

2011

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Given the occurrence frequency of any term within any set of articles within MEDLINE, we define -characteristic‖ terms as words and phrases that occur in that literature more frequently than expected by chance (at p < 0.001 or better). In this report, we studied how the cut-off criterion varied as a function of literature size and term frequency in MEDLINE as a whole, and have compared the distribution of characteristic terms within a number of journal-defined, affiliation-defined and random literatures. We also investigated how the characteristic terms were distributed among MEDLINE titles, abstracts, and last sentence of abstracts, including -regularized‖ terms that appear both in the title and abstract of the same paper for at least one paper in the literature. For a set of 10 disciplinary journals, the characteristic terms comprised 18% of the total terms on average. Characteristic terms are utilized in several of our web-based services (Anne O'Tate and Arrowsmith), and should be useful for a variety of other information-processing tasks designed to improve text mining in MEDLINE.

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Distribution of information in biomedical abstracts and full-text publications

Cited by 89 publications

References 4 publications

A quantitative model for linking two disparate sets of articles in MEDLINE

A quantitative model for linking two disparate sets of articles in MEDLINE

Gene symbol precision

Distribution of “Characteristic” Terms in MEDLINE Literatures

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