Using concepts in literature-based discovery: Simulating Swanson's Raynaud-fish oil and migraine-magnesium discoveries
By Marc Weeber, Henny Klein, Lolkje T. W. de Jong-van den Berg, Rein Vos. J. Assoc. Inf. Sci. Technol., 2001
298 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Molecular Biology
View Paper: https://www.semanticscholar.org/paper/3b06dcfd6e93a4d23d63e078e52296058dfe64fa
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Amit P. Sheth
- Krishnaprasad Thirunarayan
- Min Song
- Thomas C. Rindflesch
- Ramakanth Kavuluru
- Olivier Bodenreider
- Delroy Cameron
- Chaomei Chen
In Molecular Biology
- Amit P. Sheth
- Krishnaprasad Thirunarayan
- Ramakanth Kavuluru
- Olivier Bodenreider
- Delroy Cameron
- Thomas C. Rindflesch
- Min Song
- Chaomei Chen
Citations in the corpus
- Context-Driven Automatic Subgraph Creation for Literature-Based Discovery (2015, background): “Scenario Intermediate(s) Cameron Srinivasan [36] Weeber [34] Blake [? ] Gordon [33]”
- Visualizing a field of research: A methodology of systematic scientometric reviews (2019, background): “Significant developments have also been made by other researchers along this generic framework, for example, (24-26).”
- Review of Drug Repositioning Approaches and Resources (2018, methodology): “The origin of text mining methods in the medical field is the Swanson ‘ABC’ model, which states that if concept A is connected with concept B, and concept B is involved with concept C, then concept A may have a novel connection with concept C [65].”
- Text mining: Generating hypotheses from MEDLINE (2004, methodology): “Although the original discovery was made using the closed approach, we first apply the open procedure (Gordon & Lindsay, 1996; Weeber et al., 2001) and then the closed one (Weeber et al., 2001).”