title: Leveraging concept-based approaches to identify potential phyto-therapies.
abstract: The potential of plant-based remedies has been documented in both traditional and contemporary biomedical literature. Such types of text sources may thus be sources from which one might identify potential plant-based therapies ("phyto-therapies"). Concept-based analytic approaches have been shown to uncover knowledge embedded within biomedical literature. However, to date there has been limited attention towards leveraging such techniques for the identification of potential phyto-therapies. This study presents concept-based analytic approaches for the retrieval and ranking of associations between plants and human diseases. Focusing on identification of phyto-therapies described in MEDLINE, both MeSH descriptors used for indexing and MetaMap inferred UMLS concepts are considered. Furthermore, the identification and ranking consider both direct (i.e., plant concepts directly correlated with disease concepts) and inferred (i.e., plant concepts associated with disease concepts based on shared signs and symptoms) relationships. Based on the two scoring methodologies used in this study, it was found that a Vector Space Model approach outperformed probabilistic reliability based inferences. An evaluation of the approach is provided based on therapeutic interventions catalogued in both ClinicalTrials.gov and NDF-RT. The promising findings from this feasibility study highlight the challenges and applicability of concept-based analytic strategies for distilling phyto-therapeutic knowledge from text based knowledge sources like MEDLINE.
authors.affiliation: Department of Microbiology and Molecular Genetics, University of Vermont, Burlington, VT 05405, USA.
authors.forename: Vivekanand
authors.initials: V
authors.lastname: Sharma
authors.forename: Indra Neil
authors.initials: IN
authors.lastname: Sarkar
mesh_terms: D002986:Clinical Trials as Topic; D006801:Humans; D046650:Medical Subject Headings; D008517:Phytotherapy
keywords: Concept-based text analytics; Drug discovery; Phyto-therapies; Translational bioinformatics