Invited ArticleJournal of Scientometric ResearchVol. 8 | Issue 2s | 2019 | pp. s74–s84Open access
Treatment Repurposing using Literature-related Discovery
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Published in Journal of Scientometric Research
Correspondence: Ronald N. Kostoff
Email: rkostoff@gmail.com
Copyright: © 2019 Manuscript Technomedia. This is an open access article.
- Published:
- Nov 1, 2019
- Received:
- Oct 10, 2019
- Accepted:
- Nov 3, 2019
How to cite
Kostoff, R. N. (2019). Treatment Repurposing using Literature-related Discovery. Journal of Scientometric Research, 8(2s), s74–s84. https://doi.org/10.5530/jscires.8.2.25
Abstract
This article describes the Literature-Related Discovery technique and its application to Treatment Repurposing (which includes, but goes well beyond, Drug Repurposing). Illustrative results of potential repurposed treatments were shown from a study on preventing and reversing Alzheimer’s disease. The detailed query used to generate these results is presented. The approach has the potential to identify voluminous amounts of candidate treatments for repurposing. Additionally, a broad review of the Drug Repurposing literature is provided. A Drug Repurposing database is retrieved and the structure and content are analyzed using Text Clustering and Factor Analysis. Two taxonomies of the Drug Repurposing literature are presented and specific major themes are shown.
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Article metadata
| Title | Treatment Repurposing using Literature-related Discovery |
|---|---|
| Authors | Ronald N. Kostoff |
| Corresponding author | rkostoff@gmail.com |
| Journal | Journal of Scientometric Research |
| Volume / Issue | Vol. 8, Issue 2s (2019) |
Also in this issue
- Special Issue on Machine Learning in Scientometricspp. s1
- On the Implications of Artificial Intelligence and its Responsible Growthpp. s2–s6
- Analyzing the Common Wisdom of Binarization Doctrine in Internationality Classification of Journals: A Machine Learning Approachpp. s7–s38
- Relevance of Innovations in Machine Learning to Scientometricspp. s39–s43
- SES-RREF: The Machine Learning Approach to Credible Metrics of Scholastic Evidence via Recursive Referencingpp. s44–s73
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