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

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

Disease categories (First level of MeSH hierarchy) ranked according to the number of positive associations with spices.

<p>Numbers shown against the bars indicate the ‘number of spices’ linked with each of the associations. The number of positive disease category associations for spices outnumber those with negative associations (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0198030#pone.0198030.g007" target="_blank">Fig 7</a>) further confirming the benevolent health effects of spices.</p

Top diseases (Third level of MeSH hierarchy) ranked according to their total number of positive associations.

<p>Numbers shown against the bars indicate the ‘number of spices’ involved in the associations. The number of positive disease associations for spices outnumber the number of negative associations (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0198030#pone.0198030.g005" target="_blank">Fig 5</a>) indicating that spices, in general, have been reported with beneficial health effects.</p

Statistics of spice-disease associations.

<p>Historical trend in biomedical literature reporting spice-disease associations. There is an exponential increase in articles reporting the therapeutic effects of spices in last few decades. Data of research articles archived in MEDLINE till July 2017 is represented in the illustration.</p

The class-wise performance metrics for the best CNN model, implementing word, position, part of speech and chunk embedding features, used for spice-disease relationship extraction.

<p>All negative associations were cleaned manually.</p

Architecture of the Convolutional Neural Network.

<p>Illustration of the convolutional neural network model utilizing word, position, part of speech and chunk embeddings.</p

Statistics of positive and negative disease associations for the top 50 spices with most number of associations.

<p>Notice that certain spices like liquorice (<i>Glycyrrhiza glabra</i>) and celery <i>(Apium graveolens</i>) had equal number of positive as well as negative associations. The bias in number of associations may also indicate the inherent biases in scientific literature suggesting that certain spices are studied more than others.</p

Workflow implemented for data-driven analysis of biomedical literature associating culinary spices and herbs to diseases.

<p>Starting with compilation of an exhaustive dictionary of culinary spices and herbs, towards identification of spice-disease associations, one thread of investigation involved implementation of a computational protocol for text mining of biomedical literature including named entity recognition of herbs/spices as well as diseases, pre-processing, extraction of candidate sentences, manual annotations followed by predictions of associations with a machine learning based model. The other thread involved identification of bioactive spice phytochemicals and linking them to diseases. By integrating tripartite information of spices-phytochemicals-diseases, this study establishes the broad-spectrum benevolence of spices, suggests ways for their disease-specific culinary recommendations and probes potential molecular mechanisms underlying their therapeutic properties. Thus it provides a systems perspective to health effects of spices with potential culinary and medicinal applications.</p

FlavorDB2: An Updated Database of Flavor Molecules

Flavor is expressed through interaction of molecules via gustatory and olfactory mechanisms. Knowing the utility of flavor molecules in food and fragrances, it is valuable to add a comprehensive repository of flavor compounds characterizing their flavor profile, chemical properties, regulatory status, consumption statistics, taste/aroma threshold values, reported uses in food categories, and synthesis. FlavorDB2 (https://cosylab.iiitd.edu.in/flavordb2/) is an updated database of flavor molecules with an user-friendly interface. This repository simplifies the search for flavor molecules, their attributes and offers a range of applications including food pairing. FlavorDB2 serves as a standard repository of flavor compounds.Comment: 5 pages, 2 figure