Gaps within the Biomedical Literature: Initial Characterization and Assessment of Strategies for Discovery

Abstract

Within well-established fields of biomedical science, we identify “gaps”, topical areas of investigation that might be expected to occur but are missing. We define a field by carrying out a topical PubMed query, and analyze Medical Subject Headings by which the set of retrieved articles are indexed. Medical Subject headings (MeSH terms) which occur in >1% of the articles are examined pairwise to see how often they are predicted to co-occur within individual articles (assuming that they are independent of each other). A pair of MeSH terms that are predicted to co-occur in at least 10 articles, yet are not observed to co-occur in any article, are “gaps” and were studied further in a corpus of 10 disease-related article sets and 10 related to biological processes. Overall, articles that filled gaps were cited more heavily than non-gap-filling articles and were 61% more likely to be published in multidisciplinary high-impact journals. Nine different features of these “gaps” were characterized and tested to learn which, if any, correlate with the appearance of one or more articles containing both MeSH terms within the next five years. Several different types of gaps were identified, each having distinct combinations of predictive features: a) those arising as a byproduct of MeSH indexing rules; b) those having little biological meaning; c) those representing “low hanging fruit” for immediate exploitation; and d) those representing gaps across disciplines or sub-disciplines that do not talk to each other or work together. We have built a free, open tool called “Mine the Gap!” that identifies and characterizes the “gaps” for any PubMed query, which can be accessed via the Anne O’Tate value-added PubMed search interface (http://arrowsmith.psych.uic.edu/cgi-bin/arrowsmith_uic/AnneOTate.cgi).

A systematic review on literature-based discovery workflow

“…Moreover, recent studies have attempted to further explore alternative discovery models that deviate from the typical ABC discovery setting. These new directions include storytelling methodologies (Sebastian, Siew & Orimaye, 2017b), analogy mining (Mower et al, 2016), outlier detection (Gubiani et al, 2017), gaps characterisation (Peng, Bonifield & Smalheiser, 2017), and negative consensus analysis (Smalheiser & Gomes, 2015). For a comprehensive discussion of contemporary discovery models and future directions, please refer (Smalheiser, 2017;Smalheiser, 2012).…”

“…Identifying the important characteristics of a significant and promising association and deriving a score based on these characteristics to rank the LBD results would be more successful than merely relying on standard single measures. In this regard, the analysis of different types of gaps in the literature is useful (Peng, Bonifield & Smalheiser, 2017). Moreover, Smalheiser (2017) suggests the need of several ranking measures to customise the LBD output according to the user preferences.…”

Rediscovering Don Swanson:The Past, Present and Future of Literature-based Discovery

“…Specifically, if two topics (defined as MeSH terms) are expected to co-occur in at least 10 articles within a given field, but do not co-occur in any articles at all, we call the pair of topics a “gap”. As reported recently (Peng et al, 2017), gaps can arise for several different reasons. A few gaps reflect idiosyncracies in the rules given to MEDLINE indexers, such that certain closely related MeSH terms are rarely applied to the same article.…”

“…Randomly chosen pairs of literatures tend to have pR scores around 0.07, whereas literatures that are very closely related in terms of topics tend to have pR scores of 0.4-0.5. We have used the pR score as an important feature for literature-based discovery (Peng et al, 2017). …”

“…One may hope to identify such publications by looking for newly published articles that contain novel combinations of text terms (Packalen & Bhattacharya, 2015), novel combinations of Medical Subject Headings (Mishra & Torvik, 2016; Peng et al, 2017), or whose reference lists cite novel combinations of journals (Uzzi et al, 2013). This leads to a model of literature-based discovery that is based on link prediction on networks.…”