The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index.
Text mining: Generating hypotheses from MEDLINE
Abstract
Hypothesis generation, a crucial initial step for making scientific discoveries, relies on prior knowledge, experience, and intuition. Chance connections made between seemingly distinct subareas sometimes turn out to be fruitful. The goal in text mining is to assist in this process by automatically discovering a small set of interesting hypotheses from a suitable text collection. In this report, we present open and closed text mining algorithms that are built within the discovery framework established by Swanson and Smalheiser. Our algorithms represent topics using metadata profiles. When applied to MEDLINE, these are MeSH based profiles. We present experiments that demonstrate the effectiveness of our algorithms. Specifically, our algorithms successfully generate ranked term lists where the key terms representing novel relationships between topics are ranked high.
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Cites
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Literature Based Discovery: Models, methods, and trends
Henry, S., & McInnes, B. T. (2017) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2017.08.011 · Cited by 121
A survey on literature based discovery approaches in biomedical domain
Gopalakrishnan, V., Jha, K., Jin, W., & Zhang, A. (2019) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2019.103141 · Cited by 76
A quantitative model for linking two disparate sets of articles in MEDLINE
Torvik, V. I., & Smalheiser, N. R. (2007) · Bioinformatics · doi:10.1093/bioinformatics/btm161 · Cited by 75
LION LBD: a literature-based discovery system for cancer biology
Pyysalo, S., Baker, S., Ali, I., Haselwimmer, S., Shah, T., Young, A., Guo, Y., Högberg, J., Stenius, U., Narita, M., et al. (2019) · Bioinformatics · doi:10.1093/bioinformatics/bty845 · Cited by 65
Quantifying and filtering knowledge generated by literature based discovery
Preiss, J., & Stevenson, M. (2017) · BMC Bioinformatics · doi:10.1186/s12859-017-1641-9 · Cited by 11
Metadata
- Authors
- Srinivasan, P.
- Venue
- Journal of the American Society for Information Science and Technology
- Publisher
- Wiley
- Published
- 2003-12-16
- Volume
- 55
- Issue
- 5
- Pages
- 396-413
- ISSN
- 1532-2882 (ISSN-L)
- 1532-2890
- OpenAlex
- W2015954361
- Cited by
- 279
- References
- 33
- FWCI
- 4.61
- Type
- journal article
- Language
- en
- Open access
- closed
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
