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      Data-driven analysis of biomedical literature suggests broad-spectrum benefits of culinary herbs and spices

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          Abstract

          Spices and herbs are key dietary ingredients used across cultures worldwide. Beyond their use as flavoring and coloring agents, the popularity of these aromatic plant products in culinary preparations has been attributed to their antimicrobial properties. Last few decades have witnessed an exponential growth of biomedical literature investigating the impact of spices and herbs on health, presenting an opportunity to mine for patterns from empirical evidence. Systematic investigation of empirical evidence to enumerate the health consequences of culinary herbs and spices can provide valuable insights into their therapeutic utility. We implemented a text mining protocol to assess the health impact of spices by assimilating, both, their positive and negative effects. We conclude that spices show broad-spectrum benevolence across a range of disease categories in contrast to negative effects that are comparatively narrow-spectrum. We also implement a strategy for disease-specific culinary recommendations of spices based on their therapeutic tradeoff against adverse effects. Further by integrating spice-phytochemical-disease associations, we identify bioactive spice phytochemicals potentially involved in their therapeutic effects. Our study provides a systems perspective on health effects of culinary spices and herbs with applications for dietary recommendations as well as identification of phytochemicals potentially involved in underlying molecular mechanisms.

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          The Stanford CoreNLP Natural Language Processing Toolkit

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              The Comparative Toxicogenomics Database: update 2017

              The Comparative Toxicogenomics Database (CTD; http://ctdbase.org/) provides information about interactions between chemicals and gene products, and their relationships to diseases. Core CTD content (chemical-gene, chemical-disease and gene-disease interactions manually curated from the literature) are integrated with each other as well as with select external datasets to generate expanded networks and predict novel associations. Today, core CTD includes more than 30.5 million toxicogenomic connections relating chemicals/drugs, genes/proteins, diseases, taxa, Gene Ontology (GO) annotations, pathways, and gene interaction modules. In this update, we report a 33% increase in our core data content since 2015, describe our new exposure module (that harmonizes exposure science information with core toxicogenomic data) and introduce a novel dataset of GO-disease inferences (that identify common molecular underpinnings for seemingly unrelated pathologies). These advancements centralize and contextualize real-world chemical exposures with molecular pathways to help scientists generate testable hypotheses in an effort to understand the etiology and mechanisms underlying environmentally influenced diseases.
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                Author and article information

                Contributors
                Role: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: Formal analysisRole: InvestigationRole: MethodologyRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: Data curation
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: Project administrationRole: ResourcesRole: SupervisionRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                29 May 2018
                2018
                : 13
                : 5
                : e0198030
                Affiliations
                [1 ] Center for Computational Biology, Indraprastha Institute of Information Technology (IIIT-Delhi), New Delhi, India
                [2 ] Department of Bioscience and Bioengineering, Indian Institute of Technology Jodhpur, Jodhpur, India
                The University of Texas at El Paso, UNITED STATES
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Author information
                http://orcid.org/0000-0003-1924-6070
                Article
                PONE-D-18-02247
                10.1371/journal.pone.0198030
                5973616
                29813110
                4709c2a0-4d05-4a86-adf1-53ed53275ae9
                © 2018 Rakhi et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 22 January 2018
                : 11 May 2018
                Page count
                Figures: 9, Tables: 1, Pages: 20
                Funding
                This work was supported by a senior research fellowship from the Ministry of Human Resource Development, Government of India and Indian Institute of Technology Jodhpur to NKR. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Physical Sciences
                Chemistry
                Phytochemistry
                Phytochemicals
                Biology and Life Sciences
                Biochemistry
                Plant Biochemistry
                Phytochemicals
                Biology and Life Sciences
                Plant Science
                Plant Biochemistry
                Phytochemicals
                Biology and Life Sciences
                Organisms
                Eukaryota
                Plants
                Herbs
                Medicine and Health Sciences
                Endocrinology
                Endocrine Disorders
                Diabetes Mellitus
                Medicine and Health Sciences
                Metabolic Disorders
                Diabetes Mellitus
                Biology and Life Sciences
                Organisms
                Eukaryota
                Plants
                Garlic
                Computer and Information Sciences
                Information Technology
                Text Mining
                Biology and Life Sciences
                Biochemistry
                Antioxidants
                Computer and Information Sciences
                Information Technology
                Natural Language Processing
                Named Entity Recognition
                Computer and Information Sciences
                Neural Networks
                Biology and Life Sciences
                Neuroscience
                Neural Networks
                Custom metadata
                All relevant data are within the paper and its Supporting Information files.

                Uncategorized
                Uncategorized

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