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Leveraging concept-based approaches to identify potential phyto-therapies
Vivekanand Sharma1, Indra Neil Sarkar
1Department of Microbiology and Molecular Genetics, University of Vermont, Burlington, VT 05405, USA.
Journal of Biomedical Informatics
|May 14, 2013
Summary
This study introduces concept-based methods to find plant-based therapies (phyto-therapies) in biomedical literature. A Vector Space Model approach effectively identified plant-disease associations for potential drug discovery.
Area of Science:
- Biomedical Informatics
- Pharmacognosy
- Computational Linguistics
Background:
- Plant-based remedies (phyto-therapies) have a long history and are documented in biomedical literature.
- Concept-based analysis can uncover hidden knowledge in biomedical texts, but its application to phyto-therapy identification is underexplored.
Purpose of the Study:
- To develop and evaluate concept-based analytic approaches for identifying and ranking potential phyto-therapies from biomedical literature.
- To explore both direct and inferred relationships between plant and disease concepts.
Main Methods:
- Utilized MEDLINE database, considering both MeSH descriptors and MetaMap-inferred UMLS concepts.
- Employed two scoring methodologies: a Vector Space Model and probabilistic reliability-based inferences.
- Evaluated the approach using data from ClinicalTrials.gov and NDF-RT.
Main Results:
- The Vector Space Model approach demonstrated superior performance compared to probabilistic reliability-based inferences for identifying plant-disease associations.
- Successfully retrieved and ranked potential phyto-therapeutic relationships from biomedical text.
Conclusions:
- Concept-based analytic strategies are feasible and promising for extracting phyto-therapeutic knowledge from large text-based sources like MEDLINE.
- This approach highlights the potential for discovering novel plant-based treatments through computational analysis of existing literature.
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