Automated Recommendation of Research Keywords from PubMed That Suggest the Molecular Mechanism Associated with Biomarker Metabolites

Abstract

Metabolomics can help identify candidate biomarker metabolites whose levels are altered in response to disease development or drug administration. However, assessment of the underlying molecular mechanism is challenging considering it depends on the researcher’s knowledge. This study reports a novel method for the automated recommendation of keywords known in the literature that may be overlooked by researchers. The proposed method aided in the identification of Medical Subject Headings (MeSH) terms in PubMed using MeSH co-occurrence data. The intended users are biocurators who have identified specific biomarker metabolites from a metabolomics study and would like to identify literature-reported molecular mechanisms that are associated with both the metabolite and their research area of interest. The proposed method finds MeSH terms that co-occur with a MeSH term of the candidate biomarker metabolite as well as a MeSH term of a researcher’s known keyword, such as the name of a disease. The connectivity score S was determined using association analysis. Pilot analyses demonstrated that, while the biological significance of the obtained MeSH terms could not be guaranteed, the developed method can be useful for finding keywords to further investigate molecular mechanisms in association with candidate biomarker molecules.

Connections and Biases in Health Equity and Culture Research: A Semantic Network Analysis

“…The MeSH ontology has gained further relevance since recently, a number of researchers are using automated mining of scientific literature databases and network analysis as a novel methodology to know how the MeSH terms are related to each other and how their connectivity patterns helps better understand them—in terms of finding research ideas and raising or restating some hypotheses, and in summarizing a large amount of information ( 28 ). This method has also been useful for finding emergent keywords to further investigate in research areas such as immunotherapy and cancer ( 22 , 29 , 30 ), metabolomics ( 31 ), individual cognitive map or semantic networks ( 32 ), predictive, preventive and personalized medicine ( 33 ), biomedical sciences ( 34 , 35 ), genetic ( 36 ), and other areas of health research ( 37 – 41 ). This will be also the approach we will follow here.…”