A quantitative model for linking two disparate sets of articles in MEDLINE
Open accessAbstract
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.psych.uic.edu Contact: neils@uic.edu, vtorvik@uic.edu Supplementary information: Supplementary data are available at Bioinformatics online.
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Existing LBD methods and tools primarily quantify the strength of association without assessing relevance to or support of a specific scientific hypothesis.
“However, while existing LBD methods can efficiently identify statistically significant associations between biomedical terms, they generally lack a structured framework for evaluating whether these discovered associations meaningfully support or contradict a specific scientific hypothesis.”
From SKiM-GPT: combining biomedical literature-based discovery with large language model hypothesis evaluation (2025) · cites this paper as [34]
The Introduction says earlier approaches lacked a quantitative model for predicting which linking terms would be relevant to a given search. The Discussion says the paper introduces such a model, while the scientist ultimately decides whether a link is meaningful. These passages support the distinction between finding links and assessing relevance, but they do not establish that LBD methods generally lack a framework for evaluating whether an association supports or contradicts a specific hypothesis. (LLM, conf=0.86)
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Agresti, A. (2002) · Wiley Series in Probability and Statistics · doi:10.1002/0471249688 · Cited by 6,746 · see citing works
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Freeman, J., Millikin, R. J., Xu, L., Sharma, I., Moore, B., Lock, C., Shine George, K., Bal, A., Mohanty, C., & Stewart, R. (2025) · BMC Bioinformatics · doi:10.1186/s12859-025-06350-7 · Cited by 4 · see citing works
- Authors
- Torvik, V. I., & Smalheiser, N. R.
- Affiliations
- University of Illinois Chicago (US)
- Corresponding author
- Smalheiser, Neil R.
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2007-04-26
- Volume
- 23
- Issue
- 13
- Pages
- 1658-1665
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- OpenAlex
- W2164173383
- Cited by
- 75
- References
- 1
- Citations by year
- 2025: 1 · 2024: 1 · 2023: 8 · 2022: 2 · 2021: 3 · 2020: 5 · 2019: 10 · 2018: 2 · 2017: 4 · 2016: 3 · 2015: 4 · 2014: 6 · 2012: 2
- FWCI
- 2.38
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by-nc
- Locations
- Bioinformatics (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health
- U.S. National Library of Medicine (LM08364, R21LM008364, R01LM007292, LM007292)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › scientometrics and bibliometrics research
- Keywords
- MEDLINE, B-term, relevance prediction, logistic regression, literature-based discovery, text mining, Arrowsmith, bibliographic database searching
- MeSH terms
- Algorithms, Artificial Intelligence, Natural Language Processing, Terminology as Topic, Pattern Recognition, Automated, Periodicals as Topic, MEDLINE, Information Storage and Retrieval, Vocabulary, Controlled
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