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

Abstract

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

Distribution of “Characteristic” Terms in MEDLINE Literatures

“…In the Arrowsmith two-node search tool [19,20], the user seeks to assess a possible relationship between literatures A and C; the computer interface presents a list of terms (the -B-list‖) in common between the literatures to serve as a conceptual bridge. However, not all B-terms are likely to be of equal value in discovering significant implicit links.…”

“…However, not all B-terms are likely to be of equal value in discovering significant implicit links. Characteristic terms expressed in each literature are computed as a feature in the quantitative model that allows us to rank the B-terms in order of predicted relevance to linking the two literatures in a meaningful way [19]. Moreover, B-terms that are not characteristic in either literature A or C are unlikely to indicate important concepts in either literature, whereas B-terms that are characteristic in both A and C may represent concepts that are already well known.…”

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

“…In both cases, the MeSH terms are searched without expansion to retrieve related terms. Arrowsmith software computes the words and phrases that are in common to the titles of articles in the two queries (ie., the B-terms), and uses a quantitative model (Torvik and Smalheiser, 2007) to estimate the predicted relevance of each B-term for linking the two queries in a meaningful way. The percentage of B-terms that are predicted to be relevant is the pR score.…”

“…In previous investigations, we have studied the potential benefit of identifying disparate areas of scientific investigation which are disconnected – that is, they reside in two different sets of articles that do not share authors, and are poorly cross-cited or co-cited – yet they contain information that, when connected, leads to promising and testable new hypotheses (Swanson and Smalheiser, 1997; Torvik and Smalheiser, 2007; Smalheiser et al, 2009). The presumption is that connections between disparate areas of investigation are likely to be overlooked (due to lack of reading widely enough by scientists), neglected (e.g.…”

Scientific Discovery, Computational Models of

“…The system is still being used in the medical research community and has been extended further to improve search results. For example, Torvik and Smalheiser (2007) describe the development of a quantitative regression model that uses additional criteria to establish and rank the probable relevance of any connections found by the tool.…”